Date: March 25, 2026

Time: 9:30am to 6:30pm

Venue: Rowan University, Chamberlain Student Center

8:30am - Beverage/Snack Station Open  - Location: 1st Floor Lobby - the Student Center Commons

9:30am - Poster Set Up

9:45am - Welcome Remarks & Kickoff (Location: Student Center Expansion Commons Area)

10:am - How to Present your Research: A Workshop for Graduate Students by Matthew Heras, PhD Student (Sponsored by the Rowan University Materials Research Society) (Location: Student Center, Room 129)

11:am - Poster Symposium - Presenters should be at their posters from 11-12:pm if Poster # is ODD and 1-2pm if Poster # is EVEN

12:pm - 1:pm -Lunch (Grab and Go)  - Location: Room 153 (Upper Level) - *lunch tickets are inside of your nametag*

2:30pm - Three Minute Thesis (3MT) Competition - Location: Eynon Ballroom (ALL are welcome to attend and support the 3MT Presenter) 

3:45pm - Awards Ceremony (Location: Eynon Ballroom)

4:30pm - Graduate Student and PostDoctoral Fellow Reception (Location: Owl's Nest) 

Faculty and Staff Organizing Committee

First Name     Last Name College/School

Darren         Boehning         Cooper Medical School of Rowan University (CMSRU)

Susanne Ferrin Henry M. Rowan College of Engineering

Dianne Langford Rowan Virtua School of Translational Biomedical Engineering and Sciences

Yolanda Mack Henry M. Rowan College of Engineering

Paromita Nath Henry M. Rowan College of Engineering

Christina Newlands              College of Education (Graduate Student) and University College (Staff)

Prithvi Ravi Henry M. Rowan College of Engineering (Graduate Student Representative)

Find poster numbers sorted by last name:

Poster Number First name                Last name

98                        Nicole                         Abbott

82                        Samuel                       Addai

54                        Jamael                       Ajah

79                        Moayad                      Al Issa

15                        Mohammad               Alkhatib

85                        Noor Aldin                  Alzghoul

9                          Morgan                       Antisell

65                        Harriet Dufie              Appiah

7                          Marianne                    Assogba

71                        Anil Kumar                 Baditha

72                        Anil Kumar                 Baditha

36                        Aymen                        Bahri

12                        Rhea                           Banerjee

68                        Mahmuda Akter        Banna

78                        Kelsey                        Barker

31                        Keith                           Basgil-Koveloski

95                        Fazalay                      Beshal

73                        Mark                           Boata

3                          Lillian                          Boltz

86                        Ronald                        Borja-Roman

29                        Justin                          Burrell

32                        Brandon                     Burrell

75                        Ethan                          Cantor

45                        Giuseppina                Carannante

64                        Andres David            Castellar Freile

74                        Terry                           Coley

102                       Brendan                    Connor

1                          Jack                            Cummings

55                        Jayanto                      Das

83                        Bably                          Das

76                        Lilliana                        De Salas

92                        Joseph                       Deckhut

58                        Diane                          Duncan

6                          Shahab                      Edalatian Zakeri

66                        Evans                         Eleezar

39                        Barnabas                   Gao

48                        Attanasia                    Garuso

19                        Liza                             Guner

53                        Zubair                         Hafeez

26                        Lindsay                      Hager

100                      Ladarion                     Hardison

22                        Dominique                 Hassinger

96                        Crystal                        Hutchinson

13                        Michael                      Ingling

8                          Kaitlyn                        Insana

51                        Md Sadman               Islam

70                        Sk Md Imdadul          Islam

44                        Tarun Teja                 Kairamkonda

50                        Amine                         Khelifi

69                        Marina                        Kim

40                        Ryan                           Kleynowski

14                        Kale                            Kroenke

47                        Maria                          Lentini

56                        Tristan                        Letizia

52                        Lu                                Lin

18                        Stacy                          Love

33                        Sravani                       Malasani

61                        Heather                      Malino

28                        Christopher                Matarazzo

97                        Makaylah                   Michel

62                        Joseph                       Midiri

34                        Govinda                     Navale

27                        Viet-Linh                    Nguyen

38                        Danita                         Nti

81                        Temitope                    Okunbamu

101                      Sean                           Olcese

93                        Nicholas                     Pagliocca

37                        Nicholas                     Paradis

43                        Suprova                     Paul

84                        Tyler                           Paupst

63                        Jillian                          Peslak

25                        Vanessa                     Pizutelli

42                        Charitha                     Rathnayaka

35                        Salma Afia                 Ratri

88                        Prithvi                         Ravi

21                        Logan                         Reilley

89                        Ian                               Renaud

10                        Lina Marcela             Rosero Ordonez

16                        Dipon                          Roy

23                        Hope                           Seybold

17                        Mina                            Shahriari Khalaji

2                          Esha                           Sharma

59                        Court                          Shuller

87                        Amit                            Singh

90                        Cody                           Soper

60                        Rebecca                    Spirer

80                        Vid Liam                     Stijelja

41                        Ali                                Subhan

57                        Jacqueline                 Tawney

91                        James                        Turbett

5                          Camila                        Vardar

94                        Vaibhavsingh            Varma

67                        Eliandre Russel        Vistal

30                        Nakoa                         Webber

11                        Abigail                        White

4                          Dawn                          Williams

20                        Xavier                         Woods

77                        Cheyenne                  Woodward

24                        Joshua                       Yang

46                        Hoi Yan                      Yu

99                        AHMED IMTIAZ        ZAMEE

49                        Wen                            Zhou

Find posters sorted by College/School:

College of Education

58                  Diane                Duncan

59                  Court                Shuller

96              Crystal  Hutchinson

College of Science and Mathematics (CSM)

1                    Jack                  Cummings

3                    Lillian                Boltz

4                    Dawn                Williams

11                  Abigail               White

12                  Rhea                 Banerjee

15                  Mohammad       Alkhatib

28                  Christopher       Matarazzo

29                  Justin                Burrell

30                  Nakoa               Webber

31                  Keith                 Basgil-Koveloski

32                  Brandon            Burrell

33                  Sravani             Malasani

34                  Govinda            Navale

37                  Nicholas            Paradis

38                  Danita               Nti

40                  Ryan                 Kleynowski

41                  Ali                     Subhan

42                  Charitha            Rathnayaka

43                  Suprova            Paul

44                  Tarun Teja        Kairamkonda

47                  Maria                Lentini

48                  Attanasia           Garuso

49                  Wen                  Zhou

50                  Amine               Khelifi

52                  Lu                     Lin

53                  Zubair               Hafeez

76                  Lilliana              De Salas

77                  Cheyenne         Woodward

81                  Temitope           Okunbamu

89                  Ian                    Renaud

90                  Cody                 Soper

91                  James               Turbett

Henry M Rowan College of Engineering (HMRCoE)

5                    Camila              Vardar

6                    Shahab             Edalatian Zakeri

9                    Morgan             Antisell

10                  Lina Marcela     Rosero Ordonez

14                  Kale                  Kroenke

16                  Dipon                Roy

17                  Mina                 Shahriari Khalaji

18                  Stacy                Love

19                  Liza                  Guner

20                  Xavier               Woods

21                  Logan               Reilley

22                  Dominique         Hassinger

23                  Hope                 Seybold

24                  Joshua              Yang

26                  Lindsay             Hager

35                  Salma Afia        Ratri

36                  Aymen              Bahri

39                  Barnabas          Gao

45                  Giuseppina        Carannante

46                  Hoi Yan             Yu

51                  Md Sadman       Islam

54                  Jamael              Ajah

55                  Jayanto             Das

56                  Tristan              Letizia

57                  Jacqueline         Tawney

60                  Rebecca           Spirer

61                  Heather             Malino

62                  Joseph              Midiri

63                  Jillian                Peslak

64                  Andres David     Castellar Freile

65                  Harriet Dufie      Appiah

66                  Evans               Eleezar

67                  Eliandre Russel Vistal

68                  Mahmuda Akter Banna

69                  Marina              Kim

70                  Sk Md Imdadul  Islam

71                  Anil Kumar        Baditha

72                  Anil Kumar        Baditha

73                  Mark                 Boata

74                  Terry                 Coley

75                  Ethan                Cantor

79                  Moayad             Al Issa

80                  Vid Liam            Stijelja

82                  Samuel             Addai

83                  Bably                Das

84                  Tyler                 Paupst

85                  Noor Aldin         Alzghoul

86                  Ronald              Borja-Roman

87                  Amit                  Singh

88                  Prithvi               Ravi

93                  Nicholas            Pagliocca

94                  Vaibhavsingh     Varma

95                  Fazalay             Beshal

99              Ahmed Imtiaz Zamee

102               Brendan        Connor

Ric Edelman College of Communication, Humanities and Social Sciences

97                  Makaylah          Michel

Cooper Medical School of Rowan University (CMSRU)

2                    Esha                 Sharma

Rowan Virtua School of Translational Biomedical Engineering and Sciences

7                    Marianne           Assogba

8                    Kaitlyn               Insana

13                  Michael             Ingling

25                  Vanessa            Pizutelli

School of Earth and the Environment

78                  Kelsey               Barker

92                  Joseph              Deckhut

98                  Nicole               Abbott

100                Ladarion            Hardison

101                Sean                 Olcese

Shreiber School of Veterinary Medicine

27                  Viet-Linh           Nguyen

Find posters sorted by Topical Area / Category:

Biological Sciences, Biomedical & Health Sciences, and Biomedical Engineering

1                    Jack                 Cummings

2                    Esha                Sharma

3                    Lillian                Boltz

4                    Dawn               Williams

5                    Camila              Vardar

6                    Shahab             Edalatian Zakeri

7                    Marianne          Assogba

8                    Kaitlyn              Insana

9                    Morgan             Antisell

10                  Lina Marcela     Rosero Ordonez

11                  Abigail              White

12                  Rhea                Banerjee

13                  Michael             Ingling

14                  Kale                 Kroenke

15                  Mohammad      Alkhatib

16                  Dipon               Roy

17                  Mina                 Shahriari Khalaji

18                  Stacy                Love

19                  Liza                  Guner

20                  Xavier               Woods

21                  Logan               Reilley

22                  Dominique        Hassinger

23                  Hope                Seybold

24                  Joshua             Yang

25                  Vanessa           Pizutelli

26                  Lindsay             Hager

27                  Viet-Linh           Nguyen

102                Brendan        Connor

Chemistry & Molecular Sciences

28                  Christopher       Matarazzo

29                  Justin               Burrell

30                  Nakoa              Webber

31                  Keith                Basgil-Koveloski

32                  Brandon           Burrell

33                  Sravani             Malasani

34                  Govinda            Navale

Civil Engineering & Environmental Engineering

35                  Salma Afia        Ratri

36                  Aymen              Bahri

Computational & Theoretical Modeling

37                  Nicholas           Paradis

38                  Danita              Nti

39                  Barnabas          Gao

Computer Science, Computer Engineering, Data Science, Machine Learning & AI

40                  Ryan                Kleynowski

41                  Ali                    Subhan

42                  Charitha           Rathnayaka

43                  Suprova            Paul

44                  Tarun Teja        Kairamkonda

45                  Giuseppina       Carannante

46                  Hoi Yan            Yu

47                  Maria                Lentini

48                  Attanasia          Garuso

49                  Wen                 Zhou

50                  Amine               Khelifi

51                  Md Sadman      Islam

52                  Lu                    Lin

53                  Zubair               Hafeez

Education, Learning, and Education Leadership

54                  Jamael             Ajah

55                  Jayanto            Das

56                  Tristan              Letizia

57                  Jacqueline        Tawney

58                  Diane               Duncan

59                  Court                Shuller

60                  Rebecca           Spirer

61                  Heather            Malino

62                  Joseph             Midiri

63                  Jillian                Peslak

Environmental Science/Engineering, Renewables & Sustainability

64                  Andres David    Castellar Freile

65                  Harriet Dufie     Appiah

66                  Evans               Eleezar

67                  Eliandre Russel Vistal

68                  Mahmuda Akter Banna

69                  Marina              Kim

70                  Sk Md Imdadul  Islam

71                  Anil Kumar        Baditha

72                  Anil Kumar        Baditha

73                  Mark                 Boata

74                  Terry                Coley

75                  Ethan               Cantor

76                  Lilliana              De Salas

77                  Cheyenne         Woodward

Geology

78                  Kelsey              Barker

Materials Science & Advanced Manufacturing

79                  Moayad            Al Issa

80                  Vid Liam           Stijelja

81                  Temitope          Okunbamu

82                  Samuel             Addai

83                  Bably                Das

84                  Tyler                 Paupst

85                  Noor Aldin        Alzghoul

86                  Ronald             Borja-Roman

87                  Amit                 Singh

88                  Prithvi               Ravi

89                  Ian                   Renaud

90                  Cody                Soper

Mathematics

91                  James              Turbett

Paleontology/Paleobiology

92                  Joseph             Deckhut

Robotics, Controls & Intelligent Systems

93                  Nicholas           Pagliocca

94                  Vaibhavsingh    Varma

95                  Fazalay            Beshal

Social Sciences & Humanities

96                  Crystal              Hutchinson

97                  Makaylah          Michel

Urban Planning and Policy

98                  Nicole               Abbott

99                  AHMED IMTIAZ ZAMEE

100                 Ladarion           Hardison

101                 Sean                Olcese

1 Jack Cummings

Master's student in Complex Biological Systems

Advisor(s): Lana Kruse

College of Science and Math (CSM)

Title: Pathogen Regulation In Acorn Ants Using Self-Medication

Abstract: Self-medication, the deliberate use of external substances to alleviate pathogenic or parasitic pressure, has been observed in various ant species. Hydrogen peroxide (H₂O₂), a reactive oxygen species (ROS), can mitigate the effects of pathogens, increasing the likelihood of survival. Here, we present the first study to experimentally test the effects of two gut-derived microbes from the acorn ant Temnothorax curvispinosus—an ROS-producing bacterium and an ROS-scavenging bacterium—on ant behavior following pathogen exposure. Additional dietary options included food supplemented with hydrogen peroxide and a control diet. This study represents a full colony-level behavioral analysis, where individual ants were painted to track their feeding choices and interactions under pathogen pressure from the fungal entomopathogen Beauveria bassiana. Ants supplemented with the ROS-producing bacterium showed a clear preference for these foods when infected, fulfilling the criteria for self-medication. Full colony network connectivity differed significantly across treatments during pathogen exposure. These findings provide novel evidence linking gut microbes to self-medication behaviors and highlight their critical role in colony-level health under pathogenic stress.

2 Esha Sharma

DO student in Doctor of Osteopathic Medicine, MS-II

Advisor(s): Manoj Pandey

Rowan Virtua School of Osteopathic Medicine

Title: Novel ALDH Inhibitor and Its Potential for Multiple Myeloma Treatment

Abstract:  Aldehyde dehydrogenases (ALDH) are a family of enzymes that are responsible for the detoxification of reactive aldehydes, which lead to cell damage and death. Overexpression of ALDH is responsible for the proliferation and survival of tumor cells, making it a target for novel therapy in a
variety of cancers, including multiple myeloma (MM). We hypothesize that KS100, an ALDH inhibitor, can decrease stem cell renewal and chemotherapy resistance, leading to improved patient outcomes. To determine these effects, we measured protein expression and cell survival. Treatment with KS100 alone or in conjunction with standard therapy decreases cell survival protein expression, leading to apoptosis in non-resistant and treatment-resistant strains of MM cells.

3 Lillian Boltz

PhD student in Complex Biological Systems

Advisor(s): Maggie Pearce

College of Science and Math (CSM)

Title: Identification of Phagocytic Signals Initiating Pathological Aggregate Engulfment in Huntington's Disease

Abstract: Neurodegenerative diseases (NDs) are characterized by the progressive degeneration of neurons in the central nervous system (CNS) that result in severe cognitive and motor deficits. CNS homeostasis is maintained in part by phagocytic glial cells that respond to sites of neuronal damage. However, glia can become dysfunctional and contribute to ND progression through excess synapse removal and spread of toxic protein aggregates. Protein aggregates are inclusions containing misfolded proteins that appear in many NDs. Emerging evidence suggests that pathological protein aggregates exhibit prion-like properties in that they template the aggregation of natively-folded proteins throughout the brain. Our lab has previously reported that phagocytic glia regulate the load of pathogenic mutant huntingtin (mHTT) aggregates in a Drosophila melanogaster model of Huntington’s disease (HD). Intriguingly, a subset of neuronal mHTT aggregates have the ability to seed aggregation of wild-type HTT (wtHTT) proteins in glia via Draper-dependent phagocytosis. Draper, homolog of mammalian MEGF10, functions as a cell surface receptor to initiate downstream signaling cascades facilitating the phagocytic glial response. These findings suggest that while phagocytic glia are responsible for clearing pathological aggregates from neurons, phagolysosomal network dysfunction could promote spread of engulfed aggregates. The overall objective of this study is to elucidate the mechanisms by which phagocytic glia recognize and engulf pathological protein aggregates. To accomplish this, we will identify molecular “find-me” and “eat-me” signals that initiate the phagocytic engulfment of mHTT aggregates from neurons. Our preliminary data indicate that the “eat-me” signal phosphatidylserine (PS) is externalized on the surface of axons expressing mHTT aggregates, and that PS exposure regulates mHTT aggregate engulfment. Findings from this work will enhance our understanding of how glia respond to pathological protein accumulation in the brain and could identify new targets for therapeutic interventions in HD and other NDs.

4 Dawn Williams

PhD student in Complex Biological Systems

Advisor(s): Maggie Pearce

College of Science and Math (CSM)

Title: Determining roles for Rab10 in prion-like neurodegenerative disease mechanisms involving phagocytic glia

Abstract: Huntington's disease (HD) is a genetic disorder resulting from expansion of a polyglutamine (polyQ) stretch beyond a threshold of 36 residues in the huntingtin gene. This expansion mutation causes mutant huntingtin (mHTT) proteins to misfold and aggregate in vulnerable cells of the central nervous system. Evidence supports the hypothesis that protein aggregates associated with neurodegenerative disorders, including Alzheimer’s disease, Parkinson’s disease, and HD, spread through the brain with properties similar to infectious prions: they travel between individual cell cytoplasms and template the aggregation of soluble versions of the same protein.

Our lab has reported that mHTT spreads similarly to infectious prions. mHTT aggregates transfer between synaptically-connected neurons via phagocytic glial cells in adult Drosophila brains. The conserved scavenger receptor, Draper/MEGF10, is required for neuron to glia transfer, suggesting that neuronal mHTT aggregates are engulfed by phagocytic glia via Draper-dependent phagocytosis. However, little is known about the process following glial engulfment of mHTT. Some engulfed aggregates escape from the phagolysosomal network to seed aggregation of natively-folded wild-type HTT (wtHTT) proteins. Forward genetic screening revealed that Rab10, a small GTPase, mediates mHTT aggregate spread from neurons to glia. Rab10 has roles in vesicle maturation and trafficking in endocytic and phagocytic pathways and has been suggested to localize to phagocytic compartments such as the lysosome. Intriguingly, Rab10 is a well-known phosphorylation target of leucine-rich repeat kinase 2 (LRRK2), variants of which are associated with increased risk of PD. Several PD-associated LRRK2 variants have been reported to increase phosphorylation of Rab10 and other Rab GTPases and are associated with lysosomal dysfunction and damage. The overall hypothesis I will test is whether Rab10 and its phosphorylation by LRRK2 regulate propagation of mHTT in the brain.The overarching goal of this project is to shed light on roles for Rab10 in phagocytic glia in HD.

5 Camila Vardar

PhD student in Biomedical Engineering 

Advisor(s): Mark E. Byrne

Henry M Rowan College of Engineering

Title: Biomimetic Controlled Delivery of Nanocarriers to Prevent Proliferative Vitreoretinopathy

Abstract: Purpose: Proliferative vitreoretinopathy (PVR) develops in ~1/3 of patients after repair of retinal detachment (RD). PVR is characterized by the development of epiretinal membranes and fibrosis, which could lead to recurrent RD.Previously we identified the brain-specific angiogenesis inhibitor 1 (BAI1) on Myo/Nog cells as a target for PVR treatment.We hypothesize that extended and sustained delivery of a therapeutic agent from novel self-assembled gels to specifically deplete BAI1+ cells will mitigate PVR.
Methods: By exploiting long chain noncovalent interactions using a biomimetic strategy, we synthesized and characterized a platform of optically clear, injectable, self-assembling gels for controlled release of drug-containing nucleic acid nanocarriers. Gels contain naturally occurring polycations and polyanions with PLGA-PEG-PLGA triblock copolymers.Gel properties such as the PLGA/PEG ratio, gel solution concentration, and charged homopolymer MW and concentration were varied to tailor release properties.Characterization studies included optical clarity, critical gelation temperature and sustained drug release kinetics under flow.A PVR model developed in C57B6J mice by intravitreal injection of RPE cells and SF6 gas was used to compare cell depletion in mice injected with either a bolus dose of monoclonal antibody to BAI1 conjugated to 3DNA nanocarriers for doxorubicin (G8:3DNA:Dox) or G8:3DNA:Dox in gel.PVR grade was estimated by the number of retinal folds, areas of detachment and ERM thickness.
Results: For the first time, release has been shown to be finely controlled through both degradation and diffusion from the PLGA-PEG-PLGA matrix, and electrostatic interactions between charged chains and the negatively-charged nanocarriers. Modification of PLGA-PEG-PLGA matrices with homopolymers leads to supramolecular gel formation and release durations >6 months, compared to 28 day release from unmodified gels. PVR grade was reduced in eyes treated with G8:3DNA:Dox nanocarriers in gel compared to gel alone. The drug-induced reduction in retinal folds and number of BAI1+ cells was significant at 25 days.
Conclusions: These studies suggest that extended delivery of G8:3DNA:Dox can have therapeutically relevant effects in PVR prevention.Moreover, the gel can be adapted to deliver various types of therapeutics into the eye.

6 Shahab Edalatian Zakeri

PhD student in Biomedical Engineering 

Advisor(s): Dr. Patrick Hwang

Henry M Rowan College of Engineering

Title: Multi-target therapeutic hydrogel for diabetic foot ulcer

Abstract: Introduction: Diabetic Foot Ulcers (DFUs) are non-healing wounds occurring in up to 130 million people worldwide, often associated with inflammation, infection, ischemia, and neuropathy. With standard of care, only 30% of non-infected DFUs heal within 20 weeks. DFUs fail to progress from the inflammation to the proliferation stage. Chronic hyperglycemia exacerbates this by causing oxidative stress and forming Advanced Glycation End Products (AGEs). We hypothesize that DFUs will benefit from a multi-target therapeutic that inhibits inflammation and ROS production. Peptide-based hydrogels are biocompatible drug delivery systems that can deliver multiple therapeutics, form ECM-mimicking structures for tissue regeneration, and facilitate bioactive conjugation. In this project, we designed and characterized an anti-inflammatory peptide-based hydrogel and evaluated its effect on macrophages.
Methods: A backbone was designed with the formula CX-CO-NH2-[Sequence 1]-[Sequence 2], where Sequence 1 enables ECM-mimicking structure, solubility, and calcium-induced gelation, and Sequence 2 is serine (non-bioactive) or an anti-inflammatory peptide. Constructs were synthesized by Fmoc solid phase synthesis and characterized by MALDI. Hydrogels were prepared at 1–4% with 25 mM CaCl2. U937 cells were differentiated to M0 macrophages with PMA under high glucose, incubated with iPep (50 µM), iPep Gel (50 µM), or a small molecule inhibitor (20 µM), then polarized to M1 with LPS (20 ng/mL, 24 h). IL-1β and TNF-α were measured by ELISA; intracellular ROS by DCFDA; viability by Live/Dead assay.
Results: Peptides were synthesized with correct mass. At 2 and 4% w/v, hydrogels formed after CaCl2 addition. iPep significantly reduced IL-1β and TNF-α in M1(LPS) macrophages without affecting viability. iPep Gel comparably reduced intracellular ROS and IL-1β secretion. TEM confirmed ECM-mimicking cylindrical micelle formation unaffected by iPep conjugation.
Conclusions: We designed a peptide-based construct forming cylindrical micelles and hydrogels at 2% after calcium addition. iPep Gels maintained anti-inflammatory efficacy, reducing key cytokines and intracellular ROS in M1(LPS) U937s. Studies are underway to: 1) test constructs in primary diabetic skin cells from DFU patients, 2) evaluate in vivo efficacy in rodent diabetic wound models, and 3) combine iPep gels with angiogenic therapeutics for synergistic effect.

7 Marianne Assogba

PhD student

Advisor(s): Shanmughapriya Santhanam

Rowan Virtua School of Translational Biomedical Engineering and Sciences

Title: TBD

Abstract: TBD

8 Kaitlyn Insana

PhD student 

Advisor(s): Dr. Natalia Shcherbik

Rowan Virtua School of Translational Biomedical Engineering and Sciences

Title: 3’-End rRNA Discontinuity Profiling (3’RDP): A Novel High-throughput Sequencing Platform for Mapping Oxidative rRNA Cleavage with Single-Nucleotide Precision.

Abstract: Ribosomes translate genetic information in mRNA into proteins, making them essential for cellular homeostasis. Ribosomal RNAs (rRNAs), which form the structural scaffold and catalytic core of the ribosome, are susceptible to chemical damage, particularly under stress. Oxidative stress is a major source of such damage and is unavoidable for long-lived ribosomes. Previous work from our lab showed that low-dose oxidative stress induces site-specific Fe²⁺-dependent cleavage within the expansion segment ES7L of 25S rRNA in Saccharomyces cerevisiae. However, traditional biochemical methods lacked the resolution to detect additional cleavage sites. To overcome this limitation, we developed rRNA Discontinuity Profiling (3’RDP), a next-generation sequencing (NGS) approach that enables unbiased, single-nucleotide mapping of rRNA 3′ ends generated by cleavage. This method allows comprehensive detection and quantification of even low-abundance rRNA breaks, revealing high-resolution patterns of rRNA damage previously undetectable by conventional assays. Applying 3’RDP to S. cerevisiae under low-dose oxidative stress confirmed the known ES7L cleavage site and identified additional Fe²⁺-reactive sites in conserved regions of 25S and 18S rRNAs, including Helix 24, Helix 35, and Helix 64. A similar ES7-linked break was also observed in Candida albicans, suggesting evolutionary conservation. Together, these results show that oxidative stress produces defined patterns of rRNA fragmentation rather than random degradation.

9 Morgan Antisell

PhD student in Biomedical Engineering

Advisor(s): Sophia Orbach

Henry M Rowan College of Engineering

Title: Transcriptional Regulation of Interindividual Variability of Cytochrome P450 Expression

Abstract: Adverse drug reactions are a serious public health issue, responsible for up to 10% of hospital admissions and resulting in $30 billion dollars in additional costs annually in the United States1. Drug induced liver injury is the most prevalent cause of acute liver failure, with a fatality rate of up to 50%2.  Drug metabolism is primarily driven by six Cytochrome P450 (CYP) enzymes – CYP1A2, 2C9, 2C19, 2D6, 2E1, 3A4 – which account for the metabolism of over 70% of clinically approved drugs3. Xenobiotic metabolism is heavily influenced by individual biology, including patient factors, genetics, inflammation, and disease state. While some correlations have been drawn between drug metabolism and demographic information, the vast majority of studies on interindividual variation have centered on polymorphisms and their impact on enzyme activity. However, these reports do not consider differences in basal expression of CYPs, which can vary up to 100-fold from person to person4. This variation influences the development of adverse drug reactions and liver toxicity through changes in pharmacokinetics and accumulation of reactive metabolites. We have utilized known gene regulatory relationships, existing transcriptional network inference methods, and protein interactions to characterize population variability and identify factors that mediate CYP expression. Through these computational network-based frameworks, we are working towards describing how drivers of xenobiotic metabolism regulation alter basal regulation and induction potential at a transcriptomic level.

10 Lina Marcela Rosero Ordonez

PhD student in Biomedical Engineering 

Advisor(s): Rachel Riley

Henry M Rowan College of Engineering

Title:  Exploiting the Placenta as a Biological Barrier for Selective Placental or Fetal Drug Delivery 

Abstract: The placenta is a temporary organ that develops during pregnancy, supplying oxygen and nutrients to the fetus while removing metabolic waste through the maternal circulation. In addition to supporting fetal development, the placenta acts as a biological barrier that regulates the transfer of small molecules, proteins, and antibodies, such as Immunoglobulin G (IgG), between the maternal and fetal circulation. The selectivity of the placental barrier has been studied to understand drug delivery transport and biodistribution during pregnancy. This knowledge can be leveraged to develop targeted drug delivery systems for maternal, placental, or fetal diseases, many of which lack effective treatment options. Here, we aim to design two separate lipid nanoparticle (LNP) platforms: (1) for targeted delivery to the placenta to treat placental diseases and (2) for active transport across the placental barrier to treat fetal diseases. LNPs are promising drug delivery technologies, commonly used to encapsulate nucleic acids, protecting them from degradation and facilitating their intracellular delivery. Targeted LNPs which are LNPs engineered with surface antibodies that enable selective binding to specific receptors, have the potential to increase efficacy and decrease off-target delivery, facilitating clinical translation. In this project, we developed placental-targeted LNPs by conjugating antibody fragments that recognize placental chondroitin sulfate A (plCSA). plCSA is highly expressed on trophoblasts, which are the major cell type in the placenta, making it a suitable target to promote placental-specific expression, while minimizing off-target effects. In addition, IgG can be conjugated to the surface of LNPs to leverage Fc-receptor mediated transport pathways to treat fetal diseases. We evaluated GFP mRNA delivery via plCSA-targeted LNPs to trophoblasts in vitro, finding 2-fold increase in expression of GFP with the targeted compared with untargeted LNPs.   Additionally, we have developed a three-dimensional in vitro model using transwell inserts to study the biological interactions of targeted LNPs with the placental barrier. Ultimately, this work aims to establish safe and effective LNP-based drug delivery technologies for therapeutic applications during pregnancy, enabling controlled delivery to the placenta and fetal circulation.

11 Abigail White

PhD student in Complex Biological Systems 

College of Science and Math (CSM)

Title: Chronic stress dysregulates hippocampal TLR1 and related miRNAs

Abstract: Chronic stress results in a pro-inflammatory response and can yield serious health consequences, including major depressive disorder (MDD). MDD patients have increased pro-inflammatory factors including inflammation associated receptors compared to healthy patients. Toll-like receptors (TLRs) belong to the family of pattern recognition receptors and are key in the initiation of the inflammatory process. Exposure to stressors increases hippocampal inflammation and is associated with cognitive impairment, a core endophenotype of MDD. Hippocampal TLR1 mRNA is dysregulated in a mouse model of chronic stress; however, we do not fully understand the molecular mechanisms underlying this change. MicroRNAs (miRNAs) fine-tune gene expression by binding to the 3’UTR of their target mRNA and reduce gene expression. miRNA levels are also dysregulated in response to chronic stress. miR-146a and miR-155 are two of the miRNAs that are bioinformatically predicted to target TLR1 and are dysregulated in the hippocampus of mice following chronic stress. These findings suggest that miRNA-mediated regulation of TLR1 may be important in the context of chronic stress and could serve as a target to promote resilience to the effects of chronic stress, which is a currently unexplored mechanism.

12 Rhea Banerjee

PhD student in Pharmaceutical Chemistry 

Advisor(s): Zhihong Wang

College of Science and Math (CSM)

Title:  Targeting Protein-Protein Interactions within the MAPK Pathway for Cancer Therapy

Abstract: The Mitogen Activated Protein Kinase (MAPK) cascade, comprising RAS, RAF, MEK, and ERK, regulates essential cellular functions like cell proliferation and survival. Notably, mutations in RAF kinase and RAS proteins often hyperactivate this pathway, a common occurrence in various human cancers. We conduct comprehensive biophysical and biochemical characterization of Braftide, a peptide developed in our lab to allosterically target the RAF dimer interface. Our findings suggest that Braftide also holds promise in targeting RAF signaling by disrupting the CDC37-RAF kinase interface, presenting a novel proof-of-concept strategy. This approach offers a route to intervene in the molecular chaperone network involving Heat Shock Protein 90 (HSP90) and its co-chaperone, CDC37, both regulators pivotal in kinase-specific protein folding, stabilization, and activity. In alignment with our research focus, we also study the interaction between RAS and RAF proteins. This interaction triggers significant conformational changes in RAF, however, the specific order and mechanistic details of these interactions remain elusive. Critical to this process are the interplays among RAF regulatory domains (RAS Binding Domain (RBD), Cystine Rich Domain (CRD), Kinase Domain (KD)), governing the interaction with RAS and the consequential conformational shifts that drive RAF activation. Our research aims to elucidate these key details using in vitro biophysical methods such as Biolayer Interferometry (BLI) and deuterium exchange mass spectrometry (HDX-MS). We study the RAS-RAF interactions by accessing alterations in binding affinities and conformations within the regulatory domains upon exposure to different RAS isoforms (HRAS, KRAS, and NRAS), along with pan-RAS inhibitors currently under clinical development. Collectively, our efforts enhance understanding of the biochemical mechanisms governing RAF activation and regulation. Furthermore, our exploration of innovative approaches for targeting RAF and RAS mutations holds promise for advancing therapeutic interventions in these malignancies.

13 Michael Ingling

PhD student 

Advisor(s): Dr. Manoj Pandey

Rowan Virtua School of Translational Biomedical Engineering and Sciences

Title:  Investigating the Therapeutic Potential of Soursop (Annona muricata) Extract Against Treatment-Resistant Multiple Myeloma 

Abstract: Multiple Myeloma (MM) is an incurable plasma cell malignancy that grows in the bone marrow and co-opts the microenvironment into supporting its survival and growth. It arises from a premalignant stage known as Smoldering Multiple Myeloma (SMM), which is preceded by a benign overgrowth known as Monoclonal Gammopathy of Unknown Significance (MGUS). Importantly, clonal evolution during these stages of the disease creates a genetically heterogeneous population. This heterogeneity is largely responsible for the hardiness of MM tumors; resistant clones survive treatment, expand, and become the dominant genotype. Interestingly, the South American plant known as Soursop (Annona muricata) has anti-cancer properties and is considered a cancer treatment in traditional medicine. Investigating the effectiveness of this plant in the context of MM may lead to discoveries in MM pathobiology or even novel lead compounds. We are currently researching Soursop with the aim of elucidating how it affects MM cell lines in vitro. We source leaves from commercial products, process them into a lyophilized powder, and dissolve the powder into cell culture media to produce an aqueous extract. Cell lines are exposed to this extract for either 24 hours (Western Blots and fluorescent cytometry assays) or 72 hours (MTT viability assays). Results indicate Soursop has a cytotoxic effect on cells and lowers the expression of critical growth signals and survival proteins. The consistency of this data is a promising sign that Soursop initiates cell death via a defined mechanism, not non-specific necrosis. Further experiments will be aimed toward understanding the pathways involved in Soursop-mediated cell death and how it affects extracellular signaling in MM.

14 Kale Kroenke

PhD student in Biomedical Engineering 

Advisor(s): Rachel Riley, Lark Perez

Henry M Rowan College of Engineering

Title:  Development of Ionizable Lipid : Antibiotic Hydrophobic Ion Pairs for Improved Antimicrobial Activity Against Group B Streptococcus

Abstract: Group-B streptococcus (GBS) is the most common neonatal infection, accounting for 60,000-100,000 infant deaths globally in 2020. GBS is present in 15-30% of vaginal microbiomes, with an increased incidence in underdeveloped countries. It is contracted by the infant during vaginal childbirth or traverses intra-amniotically to infect the fetus. Standard of care for pregnant patients is high-dose intravenous administration of antibiotics, usually penicillin, during labor. However, there are significant issues with systemic intrapartum antibiotic administration, such as complicated infection and lack of consideration for preterm births or vertical transmission.
Lipid nanoparticles (LNPs) are able to protect and specifically deliver therapeutics within the body. Ionizable lipids within LNPs are designed to release therapeutic cargo in response to pH. This makes LNPs strong candidates to treat vaginal infection prior to delivery, as they can respond to the vaginal pH gradient for targeted delivery that is localized to the vaginal tract. However, encapsulation of antibiotics within LNPs is difficult due to their ability to traverse lipid membranes.
We have shown that simple hydrophobic ion-pairing (HIP) increases the efficacy of antibiotics against pathogenic bacteria, that antibiotics can be complexed with ionizable lipids, and that the resulting HIPs can be used successfully in lipid nanoparticle formulations. We hypothesize ionizable lipid:antibiotic HIPs will exhibit greater efficacy against GBS than antibiotic alone and simple counterion HIPs and will exhibit pH dependent release. Additionally, we describe two libraries of ionizable lipids we will use to assess SAR of structurally distinct lipids when complexed to penicillin and ampicillin. This platform represents both a novel approach to targeted antibiotic delivery using lipid nanoparticles and a therapeutic designed specifically to account for the unique physiological considerations of pregnancy for both the parent and fetus.

15 Mohammad  Alkhatib

PhD student in Pharmaceutical Chemistry 

Advisor(s): Thomas Keck

College of Science and Math (CSM)

Title: Developing Sigma Receptor Ligands as a Novel Strategy for Treating Pain

Abstract: Opioids are highly effective analgesics for moderate to severe pain. However, the side effects associated with opioid administration, including respiratory depression, addiction, tolerance, dependence, and constipation, limit their usage, highlighting the urgent need for a novel opioid-free strategy to treat pain. Recently, Sigma receptors, including σ₁ and σ₂, have emerged as novel non-opioid pharmacological targets for neuropathic pain due to their expression in key pain-control regions of the CNS. σ₁ and σ₂ receptors lack structural homology but share similar orthosteric binding features; both function as integral membrane-associated chaperones in the endoplasmic reticulum. Preclinical studies demonstrate that σ₁ antagonists can improve opioid-induced analgesia or act synergistically as dual µ-opioid receptor agonist/σ₁ antagonists, while σ₂ agonists produce effective opioid-independent analgesia. These outcomes emphasized an important role of these receptors in pain modulation and their potential as targets for pain therapy without the side effects associated with opioid administration. We have explored potential σ₁- and σ₂-selective scaffolds and leveraged the structure–activity relationship (SAR) study for further optimization of a novel scaffold that lacks the typical aromatic moiety tethered to a basic nitrogen found in most σ₁/σ₂ ligands. In this study, the binding affinities of the resulting compound library were evaluated using radioligand displacement assays, revealing interesting receptor selectivity patterns. Ongoing efforts include refining a QSAR model to design novel next-generation compounds that may lead to safer alternatives for pain management and a deeper understanding of sigma receptor signaling.

16 Dipon Roy

PhD student in Biomedical Engineering 

Advisor(s): Mary Staehle

Henry M Rowan College of Engineering

Title:  Mapping the Edge of Chaos in Tumor-Immune-Angiogenesis Dynamics: A Systems Framework for Treatment Design 

Abstract: Tumor progression emerges from nonlinear interactions among cancer cells, immune populations, and vascular support networks. While tumor-immune models typically assume instantaneous feedback, biological processes such as antigen presentation, clonal expansion, and angiogenesis introduce intrinsic delays that fundamentally alter system stability. Here, we develop a delay-structured tumor-immune-angiogenesis model to investigate how immune recruitment lag and vascular response delay shape global tumor dynamics. Analytical stability analysis reveals that increasing immune delay induces Hopf bifurcations, leading to sustained oscillations and, at larger delays, deterministic chaos. Mapping immune amplification strength and delay parameters identifies a structured phase diagram containing stable, oscillatory, chaotic, and runaway growth regimes.
We further show that periodic therapeutic forcing interacts nonlinearly with intrinsic delays. Frequency sweeps reveal resonance windows in which modest changes in treatment cadence induce bifurcation cascades and chaotic transitions. Largest Lyapunov exponent landscapes confirm the existence of edge-of-chaos bands characterized by maximal trajectory sensitivity. Importantly, adaptive cadence modulation suppresses chaotic oscillations and restores stable dynamics without increasing treatment intensity.
These results demonstrate that tumor-immune ecosystems can operate near critical dynamical boundaries where small perturbations produce large outcome variability. Immune delay emerges as a bifurcation control parameter, while therapy timing acts as an external resonance driver. By framing relapse and treatment variability as consequences of delay-induced nonlinear instability, this work establishes a mechanistic foundation for chaos-aware therapy scheduling and dynamical stratification in precision oncology.

17 Mina Shahriari Khalaji

Post-Doctoral Fellow in Biomedical Engineering

Advisor(s): Mei Wei

Henry M Rowan College of Engineering

Title:  A Mechanism-Targeted Multifunctional Barrier for Post-Surgical Abdominal Adhesions 

Abstract: Intraperitoneal adhesions (IAs) are a common and unresolved complication following abdominal surgery, driven by early mesothelial activation, oxidative stress, and subsequent fibrotic remodeling. Current adhesion barriers act as passive mechanical separators and fail to therapeutically target the biological pathways responsible for adhesion initiation and extension. In this work, we present a multifunctional, mechanism-targeted therapeutic barrier designed to modulate key drivers of IA pathogenesis. The system features a sandwich-structured construct composed of an ultra-thin electrospun core integrated with dual bioadhesive microneedle layers. The electrospun layer, fabricated from polylactic acid/polyethylene glycol (PLA/PEG) incorporated with tannic acid, provides mechanical stability, sustained physical separation, and reactive oxygen species (ROS) scavenging to attenuate oxidative stress-mediated mesothelial activation. The outer catechol-grafted chitosan microneedles enable localized delivery of diltiazem to inhibit mesothelial cell bridge formation, promote hemostasis, and provide intrinsic antibacterial activity. The barrier exhibited strong wet-tissue adhesion (2.9±0.3 kPa) and interfacial toughness (11.75±0.5 kPa), ensuring stable retention in dynamic intra-abdominal environments. In vitro and ex vivo studies demonstrated significant suppression of early mesothelial bridging, reduced collagen deposition (p≤0.001), and pronounced antioxidant and antibacterial effects (p≤0.001), with complete degradation within 13.1±0.6 days. Ongoing in vivo studies are evaluating therapeutic efficacy and adhesion prevention in clinically relevant surgical models, supporting the translational potential of this targeted therapy.

18

Stacy  Love

Post-Doctoral Fellow in Biomedical Engineering 

Advisor(s): Sebastian Vega

Henry M Rowan College of Engineering

Title:  Sequential Macrolide Pretreatment Enhances Osteogenic Peptide-Induced BMP Signaling

Abstract: Introduction
Macrolide immunosuppressants FK506 (Tacrolimus) and Rapamycin (Sirolimus) bind FKBP12, an intracellular regulator that restrains bone morphogenetic protein (BMP) receptor activation. By sequestering FKBP12, both compounds sensitize BMP receptors to osteogenic ligands but also engage distinct secondary pathways. FK506 inhibits calcineurin, favoring SMAD-driven transcription, while Rapamycin inhibits mTORC1, enhancing ERK/AP-1-linked mechanotransductive signaling. Synthetic BMP-2 mimetic peptides, KIPKA (knuckle epitope) and DWIVA (wrist epitope) selectively activate these SMAD- and ERK- associated pathways, respectively. We hypothesized that macrolide pretreatment followed by peptide exposure would enhance BMP signaling compared to simultaneous dosing.

Methods
Pre-osteoblastic MC3T3-E1 cells expressing GFP reporters were used to quantify BMP signaling: BRE-GFP (SMAD-dependent) and SRE-GFP (ERK/AP-1-dependent). Serum-starved cells were exposed to osteogenic peptides, KIPKA (GCGGGKIPKASSVPTELSAISTLYLG) or DWIVA (GCGGGDWIVAG) (0.5-2 mM), after FK506 (5-25 µM) or Rapamycin (50-250 nM) pretreatment. GFP expression was measured by flow cytometry after 22-24 hours. Optimal concentrations (FK506 10 µM; Rapamycin 100 nM) were determined by dose-response profiling.

Results
Distinct pathway-specific selectivity was observed: KIPKA primarily activated BRE-GFP, while DWIVA activated SRE-GFP. Macrolide pretreatment enhanced these responses relative to peptide-only or simultaneous treatments. FK506 pretreatment increased KIPKA-induced BRE-GFP fluorescence, consistent with stronger SMAD signaling via calcineurin inhibition. Similarly, Rapamycin pretreatment before DWIVA exposure elevated SRE-GFP activity, reflecting potentiation of ERK/AP-1 signaling through mTORC1 inhibition. These findings demonstrate that timing-dependent FKBP12 modulation selectively enhances peptide-driven osteogenic signaling (US Provisional Application 63/866,043, 2025).

Conclusions
Sequential macrolide pretreatment strengthens osteogenic peptide signaling by first releasing FKBP12-mediated inhibition and then initiating more efficient receptor activation. FK506 preferentially augments KIPKA-SMAD signaling, whereas Rapamycin enhances DWIVA-ERK/AP-1 signaling, establishing pathway- and timing-specific mechanisms for macrolide potentiation. Ongoing work will extend these findings to 3D hydrogel systems and in vivo bone-repair models to optimize peptide-macrolide co-delivery for localized bone regeneration.

19 Liza Guner

PhD student in Biomedical Engineering 

Advisor(s): Rachel Riley

Henry M Rowan College of Engineering

Title:  Engineering Lipid Nanoparticles for Delivery of siRNA in Pediatric Acute Myeloid Leukemia

Abstract: Pediatric acute myeloid leukemia (AML) is the second most prevalent cancer in children. Children diagnosed with this disease face a 70% survival rate and a 25-35% chance of relapse. Current standard-of-care includes chemotherapy and bone marrow transplantations. While these therapies yield desirable results in many patients, some do not respond, their disease becomes resistant to further therapy, or the delivered dose is limited due to severe side effects.  Recently, immunotherapies, such as chimeric antigen receptor (CAR)-T cells, have shown promising results in clinical and pre-clinical testing for treating pediatric AML. However, long manufacturing times, expensive production, and severe adverse toxicities, such as cytokine release syndrome, limit the widespread implementation of these therapies. Therefore, there is a need for new therapeutics that are effective and safer than current options. In this project, we are developing a nanoparticle-based therapy for gene regulation in AML. Towards this goal, we use lipid nanoparticles (LNPs) to deliver therapeutic nucleic acids to specific cells and tissues. LNPs have been tested both pre-clinically and clinically for a range of diseases and for vaccines, and they are FDA approved for several applications. Thus, we have chosen to use LNPs in this project due to their translatability, efficacy, and safety in humans and animal models. We have evaluated a library of LNPs to deliver fluorescently tagged scramble siRNA to pediatric AML cells. We demonstrated that LNPs yield high siRNA delivery to AML cells after 4 hours. From this library, we determined a top-performing LNP and replaced the scramble siRNA with therapeutically relevant β-catenin siRNA. β-catenin is often overexpressed in pediatric AML and causes increased cell proliferation. With this approach, we hypothesize that β-catenin siRNA will silence β-catenin to decrease AML proliferation and survival. Moving forward, we will target these LNPs to selectively deliver to AML cells and minimize off-target effects. Ultimately, we aim to demonstrate that LNPs can initiate robust gene regulation to halt AML progression and improve survival in patients.

20 Xavier Woods

PhD student in Chemical Engineering 

Henry M Rowan College of Engineering

Title: Engineering Cell-Cell Communication Using 3D Bioprinting and EXPECT Hydrogel

Abstract: Cell–cell communication plays an important role in how tissues organize, migrate, and change behavior over time. Many of these interactions occur in structured three-dimensional environments where spatial organization and signaling gradients influence how cells move and interact. However, traditional laboratory models often lack this spatial control, making it difficult to study these processes in a controlled and repeatable way.

This work focuses on developing a 3D in vitro platform to study spatial cell interactions using the EXPECT thermoresponsive embedded bioprinting system. EXPECT hydrogels provide a supportive 3D environment that allows cells to be printed in defined spatial arrangements within the material. The thermoresponsive properties of the hydrogel also allow controlled temperature changes that temporarily soften the gel. These short cooling cycles, referred to as actuation, may influence how cells migrate and interact within the construct.

The initial phase of the study focuses on optimizing spatial parameters within the printed constructs. Cell spheroids are printed within the hydrogel and organized at different spacings by varying overall cell density. Spheroid size is limited to approximately 100–150 µm to maintain oxygen diffusion and cell viability. Constructs are maintained under either static or actuated conditions and evaluated using fluorescence imaging and metabolic assays.

These experiments are intended to identify spatial conditions that best support controlled investigation of cell–cell interaction dynamics in three-dimensional culture systems. The platform may provide a useful framework for studying how spatial organization influences cellular communication in complex tissue-like environments.

21 Logan Reilley

Master's student in Biomedical Engineering

Advisor(s): Rachel Riley

Henry M Rowan College of Engineering

Title:  Development of a Lipid Nanoparticle Treatment for Co-Delivery of mRNA and siRNA Against Preeclampsia

Abstract: Preeclampsia (PE) affects 2-8% of pregnancies and causes life-threatening conditions such as seizure, stroke, fetal growth restriction, and death. Despite being a leading cause of maternal mortality, severe PE has no specific treatments and often requires preterm birth, which can have detrimental short- and long-term effects on infant development. While the pathology of PE is not entirely understood, improper trophoblast invasion and spiral artery remodeling in early pregnancy leads to a hypoxic placental microenvironment, which stimulates the release of soluble fms-like tyrosine kinase 1 (sFlt-1). sFlt-1, a truncated form of the vascular endothelial growth factor receptor 1 (VEGFR1), acts as an antiangiogenic factor by sequestering VEGF and placental growth factor (PlGF) and attenuating their angiogenic signals. A high ratio of sFlt-1:PlGF in the maternal serum is an indicator of PE risk, making sFlt-1 and PlGF potential therapeutic targets. Here, we use lipid nanoparticles (LNPs) encapsulating sFlt-1 siRNA and PlGF mRNA to demonstrate modulation of these factors in trophoblasts cultured in normal and hypoxic oxygen conditions. LNPs are efficient drug delivery tools, used to protect RNA from degradation and increase intracellular delivery to target cells and tissues. Our lab previously developed LNP platforms that have preferential delivery to the placenta, which successfully minimize hepatic delivery and avoid fetal exposure. Using these platforms, we deliver sFlt-1 siRNA and PlGF mRNA to trophoblasts, demonstrating a 60% decrease and 1,500-fold increase in sFlt-1 and PlGF secretion, respectively. Furthermore, co-delivery of LNPs encapsulating these RNAs showed successful modulation of both factors simultaneously. Delivery of LNPs under hypoxic conditions had similar effects on sFlt-1 and PlGF expression, indicating the ability to modulate these factors in the hypoxic, preeclamptic placenta. These results show the LNPs described herein are a promising advancement in PE treatment. 

22 Dominique Hassinger

PhD student in Biomedical Engineering 

Advisor(s): Vince Beachley PhD, Sean McMillan DO

Henry M Rowan College of Engineering

Title:  Exploring fabrication, mechanics and antimicrobial effects of peptide-functionalized nanoyarns as sutures

Abstract: Sutures are commonly used to hold tissues together; however, they offer little in terms of enhanced infection prevention or tissue regeneration. As result, current su-tures that allow tissue approximation can lead to poorer healing outcomes and more scar tissue formation com-pared to materials that actively limit infections and pro-mote native tissue regrowth. Current attempts to prevent infection are limited to prophylaxis, dip coating sutures, and clean room procedures in the operating room. A functionalized material which promotes tissue regeneration and prevents infection would be highly desirable suture material.

Nanofibrous materials offer unique advantages in bio-medical engineering. Nanofibers are known to support the body’s natural healing process by influencing immune cells to promote regeneration rather than inflammation. The size of nanofibers also mimics the size of protein fibrils found in the extra-cellular matrix leading to favorable cell interaction such as enhanced attachment, proliferation, and differentiation. Though possessing these desirable biological properties, nanofibers are inherently weaker than microfibers.

Post drawing fibers in line, along with fabrication of more complex structures can give more strength to nano-fibrous materials. Producing bundled nanofibers— nanoyarns – are attractive for suture applications and pep-tide functionalization; however, there are challenges with current nanoyarn fabrication techniques. High-speed nanofiber production often leads to poor fiber alignment, uneven yarn structure, and difficulty producing lengths suitable for commercial use.
 
This study investigates a parallel track system with in-line post-drawing for nanoyarn production, focusing on strategies to improve fabrication scalability and evaluat-ing the effects of antimicrobial peptide functionalization in vitro. Additionally, suture-specific testing was per-formed to assess the potential of these nanoyarns as su-ture materials.

23 Hope Seybold

PhD student in Biomedical Engineering 

Advisor(s): Dr. Mark Byrne

Henry M Rowan College of Engineering

Title: FREEDOM(TM) Lens: A Novel Extended-Release Silicone Hydrogel Contact Lens for Sustained Ocular Drug Delivery

Abstract: Enhanced ocular drug delivery systems have significant potential to improve treatment efficacy and patient quality of life. Topical eye drops dominate the ocular pharmaceuticals market (~90%), but are grossly inefficient, with less than 5% of the administered dose reaching intraocular tissues due to rapid tear turnover and poor ocular retention. Contact lenses offer a promising alternative due to their intimate contact with the ocular surface. However, achieving sustained therapeutic drug release while maintaining all commercial lens properties has remained a barrier to clinical translation.
We developed the FREEDOMTM Lens, an extended-release silicone hydrogel contact lens that meets all commercial design specifications while enabling sustained drug delivery through a novel macromolecular memory-controlled release mechanism pioneered by our group. Release is strictly controlled by molecular-level biomaterial engineering to regulate drug-polymer interactions and transport.
In vivo studies in New Zealand white rabbits demonstrated continuous therapeutic bromfenac tear concentrations of 256.4 ± 23.1 µg/mL for eight days, compared with topical Bromday™ eye drops which produced a rapid peak concentration (213 ± 88 µg/mL) lasting less than 100 minutes. Bioavailability (AUC₀–₈days) and mean residence time were 26-fold and 155-fold greater, respectively, than topical administration. Slit-lamp examinations and corneal histology revealed no evidence of inflammation, epithelial damage, or adverse tissue responses.
A preliminary human pilot study further demonstrated successful lens wear and sustained bromfenac detection in tear samples over multiple days, supporting translational feasibility and patient tolerability.
These results demonstrate, for the first time, extended multi-day delivery of a small-molecule therapeutic from a silicone hydrogel contact lens while maintaining commercial specifications. The FREEDOMTM Lens has enormous clinical potential for treating ocular inflammation (e.g., cataract surgery, uveitis, corneal injury) via a 7-day continuous-wear therapeutic lens. This platform technology can be broadly applied to numerous ocular therapeutics, enabling sustained drug delivery, improved bioavailability, and improved patient outcomes.

24 Joshua Yang

PhD student in Biomedical Engineering 

Advisor(s): Rachel Riley

Henry M Rowan College of Engineering

Title:  Design and Evaluation of Ionizable Lipids with Variable Numbers of Ether Groups and Alkyl Tail Lengths

Abstract: Lipid nanoparticles (LNPs) serve as drug delivery platforms for nucleic acids, such as messenger RNA (mRNA) and small-interfering RNA (siRNA), to modulate gene expression. A critical component of LNPs is the ionizable lipid due to their pH-responsiveness, which enables efficient delivery of nucleic acids to cells. The ionizable lipid chemical structure significantly influences the efficiency and biodistribution of nucleic acid delivery. Here, we designed and synthesized a series of ether-containing ionizable lipids, each with different numbers of ether groups and lipid alkyl tail lengths. Our goal was to identify structure:function relationships between the number of ether groups and tail length on delivery efficiency and tissue- and cell-specific delivery. Each ionizable lipid was mixed with other components and mRNA to form a library of 10 chemically unique LNPs. Several ionizable lipids and LNPs had physicochemical characterizations comparable to gold-standard LNP formulations. Following characterization, we evaluated mRNA delivery across various cell lines and in wild-type mice. This revealed lead ionizable lipid and LNP candidates that enable high delivery both in vitro and in vivo. Moreover, we identified several ionizable lipid structures that can control organ-tropism. For example, one of our LNPs is able to bypass the liver and primarily accumulate and deliver mRNA in the spleen. Additionally,  several LNP formulations are able to deliver to the placenta with no delivery to the fetus. Together, our results demonstrate that these novel ether-ionizable lipids can be complexed into lipid nanoparticles to enhance RNA delivery efficiency and tissue targeting, offering substantial opportunity for further development as therapeutics, such as cancer immunotherapy or placenta-specific diseases. 

25 Vanessa Pizutelli

DO/PhD in Cell and Molecular Biology (DO/ PhD) 

Advisor(s): Dimitri Pestov

Rowan Virtua School of Translational Biomedical Engineering and Sciences

Title:  Distinct Ribosomal RNA Fragmentation Signatures as Potential Biomarkers for Ischemia/Reperfusion Injury

Abstract: Ischemia/Reperfusion Injury (IRI) is a significant clinical challenge and main contributor of morbidity in conditions such as myocardial infarction, acute kidney disease, and ischemic stroke. Despite efforts to develop therapies that target known IRI pathways, clinical trials have largely been unsuccessful and effective therapeutic interventions remain limited. Therefore, investigating alternative mechanisms and biomarkers associated with IRI is imperative. During IRI, cellular stress promotes metabolic dysregulation, reactive oxygen species (ROS) production, and the activation of inflammatory responses, all of which damage macromolecules that are vital for cell survival. Ribosomal RNA (rRNA), an abundant biomolecule found in ribosomes, is highly susceptible to oxidative damage as well as a known target for inflammatory endonucleases like RNase L. Using Northern blot analysis, we compared rRNA fragmentation in A549 cells exposed to oxygen/glucose deprivation (OGD), a model that mimics IRI conditions, and to other oxidative stressors including cadmium, arsenite, and erastin. These studies revealed fragmentation patterns that were unique in the OGD treated group compared to the other oxidative stressors. Furthermore, these OGD-specific 28S and 18S rRNA fragmentation patterns were altered in RNase L knockout cell lines, indicating that some of the observed rRNA strand cleavage events were mediated by RNase L activation. While Northern blots provide a global view of rRNA integrity, they lack the resolution required to determine specific cleavage sites. We therefore developed a next-generation sequencing (NGS) pipeline to pinpoint these breaks at a nucleotide-level resolution. Our results demonstrate that OGD produced unique cleavage “hot spots” that were distinct from those induced by other stressors. By defining these RNase L-dependent and independent processes, this work provides critical insight into the molecular mechanisms of IRI-induced damage and establishes a framework for developing highly specific, non-invasive rRNA-based biomarkers for tissue injury.

26 Lindsay Hager

PhD student in Biomedical Engineering 

Advisor(s): Rachel Riley

Henry M Rowan College of Engineering

Title:  Extrahepatic Nucleic Acid Conjugate Technology for Local Immunomodulation in the Placenta

Abstract: Clinical use of cytokine therapies is a powerful strategy to modulate immune activity in autoimmune diseases and cancers, limited by rapid clearance, off-target exposure, and severe systemic toxicity. Lipid nanoparticles (LNPs) are nucleic acid delivery platforms, comprised of ionizable lipids, phospholipids, cholesterol, and lipid-conjugated poly(ethylene) glycol for stability and facilitation of endosomal escape. Changing the chemical composition and molar ratio of each lipid component alters the delivery efficiency and tissue-specific biodistribution. However, a major limitation in LNP development is their natural accumulation in the liver, leading to low delivery to extrahepatic tissues and limiting local cytokine secretion .To improve tissue-specific delivery and direct local immunomodulation, we developed a novel mRNA-based platform, Extrahepatic Nucleic Acid Conjugate Technology (ENACT), a synthetic mRNA construct that encodes an immunologically active cytokine coupled to a targeting peptide sequence. When encapsulated in LNPs, ENACT is translated and secreted by tissue-resident cells, creating a self-targeting secreted cytokine that enters circulation and homes to the targeted tissue. This exploits innate hepatic delivery of LNPs, placing the targeting burden on the therapeutic peptide itself and minimizes systemic cytokine exposure by enabling high concentrations of immunomodulatory cytokines at the targeted site. Here, we demonstrate feasibility of this concept by designing ENACT encoding interleukin-4 (IL-4) as the active cytokine moiety and placental CSA (plCSA) binding peptide as the targeting moiety, with the goal of mediating inflammatory conditions of preeclampsia, a pregnancy-related disorder. When administered to trophoblasts in vitro, which express plCSA, ENACT is secreted and binds to the cell surface. Furthermore, we show the ENACT cytokines translated and secreted by liver cells in vitro can polarize anti-inflammatory macrophages when treated with conditioned media. We demonstrate that ENACT IL-4 translated in the liver (and/or spleen) are released into circulation, and home to the placenta, where it polarizes macrophages, inducing an anti-inflammatory phenotype. ENACT provides a clinically translatable mechanism, exploiting hepatic delivery to enable local immunomodulation, reducing systemic toxicities of cytokine therapies.

27 Viet-Linh Nguyen

Post-Doctoral Fellow in Veterinary Medicine

Advisor(s): Pratap Kafle

Rowan-Virtua School of Osteopathic Medicine

Title:  Molecular Xenomonitoring for Early Detection of West Nile Virus Circulation in New Jersey: A Field Validation Study

Abstract: West Nile virus (WNV) remains a persistent public health concern in New Jersey, yet current pooled-mosquito PCR surveillance often detects viral activity only after significant amplification, narrowing the window for timely intervention. Molecular xenomonitoring (MX), which is the detection of viral RNA in mosquito excreta or saliva deposited on honey-baited FTA cards, offers a non-destructive alternative that preserves nucleic acids at room temperature, passively captures signals from hundreds of mosquitoes per card, and has the potential to detect WNV circulation earlier than conventional surveillance by continuously sampling mosquito activity without the need for lethal collection. This project aims to (1) analyze the last 20 years of statewide mosquito surveillance and climate data to characterize spatial-temporal trends in WNV transmission and vector species composition across urban, suburban, and rural landscapes, and use these findings to select the 15 most appropriate sampling sites based on historical WNV infection rates and vector abundance; (2) evaluate MX as a scalable, cost-effective complement to standard pooled-mosquito RT-qPCR at these sites; and (3) apply metagenomic sequencing to FTA cards to detect the full spectrum of mosquito-transmitted pathogens beyond WNV.
Preliminary analysis of historical mosquito surveillance and climate data reveals a widening seasonal transmission window for WNV alongside notable shifts in vector species composition across different landscape types. These trends suggest that current fixed-window surveillance protocols may be inadequate for capturing emerging transmission dynamics across New Jersey's heterogeneous environments.
Beginning May 2026, modified gravid traps with FTA cards will be deployed at the 15 selected sites and serviced every four days. RNA extracted from FTA cards and paired mosquito pools will be tested for WNV via RT-qPCR and results aligned with publicly available NJ epidemiological indicators. Metagenomic sequencing of FTA cards will additionally be performed to identify co-circulating and emerging mosquito-borne pathogens present in saliva and excreta across landscapes.
This study will produce the first field-validated MX framework for NJ, determining whether MX delivers earlier viral signals and reduces laboratory workload relative to conventional methods. Metagenomic data will provide a baseline characterization of the mosquito-associated pathogen community statewide.
Shifting climate patterns and evolving vector communities demand more adaptive surveillance tools. By integrating MX into NJ's existing infrastructure, this work has the potential to improve early warning capacity, reduce response time to emerging arboviral threats, and establish a replicable model for cost-effective, multi-pathogen mosquito surveillance applicable across the northeastern U.S.

102 Brendan Connor

Master's Student in Biomedical Engineering

Advisor(s): Mark Bryne

Henry M. Rowan College of Engineering

Title: Controlled Drug Release from Engineered Nucleic Acid Monolayers

Abstract: As cancer remains a leading cause of death worldwide, new treatment modalities are imperative to improve patient survival and quality of
life. Intravenously administered chemotherapy remains a ubiquitous treatment for virtually all forms of cancer but is limited by several challenges, including a lack of target specificity. More recently, nanomedicines have begun to reshape the way drugs are distributed
throughout the human body, leading to increased bioavailability and efficacy. Previous work in our group has explored a novel drug delivery platform composed of daunomycin-loaded oligonucleotides conjugated to gold nanoparticles via 5′ thiol bonds. However, further work is required before this intervention can become clinically viable. One key objective is the full elucidation of variables that affect drug release from DNA monolayers, including oligonucleotide density, sequence composition, and length. We hypothesize that variations in these parameters will enable greater control over drug release. In this work, we demonstrate the successful use of QCM-D to model monolayer formation, drug binding, and drug release, thereby establishing the role of DNA monolayer density and relative monolayer thickness in controlling drug release. These results suggest that higher grafting densities of DNA enable greater control over drug release. Furthermore, larger DNA grafting density is correlated with increased monolayer thickness, supporting the hypothesis that DNA monolayers delay drug release via physical obstruction and re-intercalation. Taken together, this work conveys the emerging importance of oligonucleotides in the fight against cancer and other recalcitrant diseases.

28 Christopher Matarazzo

PhD student in Pharmaceutical Chemistry 

Advisor(s): Subash Jonnalagadda

College of Science and Math (CSM)

Title:  Novel Small Molecules as Anti-Cancer Agents

Abstract: Triple negative breast cancer (TNBC) constitutes 10-20% of all breast cancers and is associated with aggressive tumor growth, metastasis, and poor patient outcome. Despite
numerous advances in tumor diagnosis, surgical procedures, and radio/chemotherapy, prognosis has not improved significantly for patients with TNBC over the past 30 years.
About 20–30% of TNBC patients have a proven BRCA1/2 mutation and breast cancers, particularly those with BRCA1/2 mutations, are sensitive to poly ADP-ribose polymerase
(PARP) inhibitors such as olaparib. While PARP inhibitors have improved the treatment for patients with the mutant BRCA gene, the clinical outcome is not as expected as only a
small subset of TNBC patients have BRCA1/2 deficiency. Although the incidence of TNBC is high in African-American women, several studies show that the incidence of germline BRCA1 mutations is low relative to the incidence in White women. This suggests that other genetic mechanisms beyond germline mutation of BRCA1 may promote TNBC in African-American women. Therefore, targeting additional signaling pathways which can sensitize TNBC to PARP inhibitors may offer new avenues for TNBC treatment. It has also been demonstrated that Wnt/β-catenin signaling pathway is preferentially activated in TNBC.

Salinomycin is a polyether antibiotic used in animal farming as an anti‐coccidiostat and is produced by Streptomyces albus. Recently, salinomycin was identified as one of
the most potent inhibitors of breast cancer stem cells based on a high‐throughput screening of compounds with several fold higher potency than the anti‐cancer drug Paclitaxel.
Salinomycin has also been shown to induce apoptosis in many human cancer cells. The mechanism of action is still not fully understood. The WNT/β‐catenin pathway is a
critical process responsible for the cancer stem cell survival, and salinomycin is also known to inhibit cell growth in several WNT dependent cancer cells in vitro. Accordingly, we
took up a project involving the design and synthesis of novel analogs of salinomycin for development as potential anti-TNBC agents.

29 Justin Burrell

Master's student in Pharmaceutical Sciences 

Advisor(s): Timothy Vaden

College of Science and Math (CSM)

Title:  Micelle properties and lipid bilayer permeabilities of cholinium and tetramethylguanidinium laurate ionic liquids

Abstract: Ionic liquids (ILs) that can form micelles in aqueous solution have potential applications as pharmaceutical formulation ingredients due to their abilities to encapsulate drug molecules and permeabilize lipid bilayer membranes. Fatty acid ILs (FAILs) in which the anion is a fatty acid are of particular interest as they are often non-toxic and hence​ biocompatible. In this presentation we show results from lipid membrane permeability assays that quantify the abilities of different FAILs to permeabilize lipid nanoparticles. Combined with critical micelle concentration measurements, the results demonstrate how FAIL micelle formation can correlate with increased lipid membrane permeability. These results will be helpful in designing and optimizing FAIL-based drug formulations

30 Nakoa  Webber

PhD student in Complex Biological Systems 

Advisor(s): Nathaniel Nucci

College of Science and Math (CSM)

Title:  Structure-function relationships of Sigma-1 receptors for therapeutic targeting

Abstract: Sigma-1  receptors (S1Rs) are non-opioid, transmembrane proteins with implications across a broad variety of cell functions and disease states. They are a desirable drug target for their roles  in opioid mediated analgesia, cancer, and neurological disorders. S1Rs facilitate physiological and pharmacological processes through interactions with g-protein coupled receptors (GPCRs), ion channels, and additional cell signaling factors. While agonists and antagonists have been identified to alter S1R function, they are most frequently described using animal models without a known molecular mechanism, making ligand classification challenging. S1Rs have been found in monomeric, dimeric, and higher order oligomeric states in the cell with evidence that ligands bind to various oligomeric states. S1Rs have been reported to favor higher order oligomers while bound to antagonists, while agonist-bound structures favor heterodimers. We are working to characterize structural changes and how they relate to ligand classification and influence S1R protein-protein interactions. S1R was cloned into a pcDNA3.1- plasmid vector, inducibly overexpressed expi293 cells, and purified using FLAG-affinity chromatography.  Using co-immunoprecipitation and liquid chromatography-mass spectrometry techniques we have identified ligand-dependent changes in the S1R interactome. Characterization of ligand binding effects on S1R oligomerization and protein-protein interactions will improve our overall understanding of sigma receptor biology and reveal novel molecular relationships worth targeting therapeutically.

31 Keith Basgil-Koveloski

PhD student in Pharmaceutical Chemistry 

Advisor(s): Subash Jonnalagadda

College of Science and Math (CSM)

Title:  Rapid Authentication of Ganoderma Species and Strains Using Handheld Near-Infrared Spectroscopy and Machine Learning

Abstract: Ganoderic acids, key bioactive compounds in Ganoderma species, are traditionally analyzed using liquid chromatography-mass spectrometry (LC-MS). 1 However, LC-MS methods are time-consuming and take up to two hours per sample for analysis.2,3 To address this limitation, we are developing a novel approach leveraging handheld near- infrared spectroscopy (NIRS) to rapidly assess the composition of different Ganoderma batches without the need for sample preparation or lengthy chromatographic analysis. Initial testing of 100 Ganoderma samples demonstrates that NIRS, combined with machine learning algorithms, can effectively differentiate between different Ganoderma species. Ongoing research aims to further correlate NIRS spectral data with LC-MS quantifications of individual ganoderic acids, enhancing the accuracy and applicability of this rapid screening method. This approach has the potential to significantly accelerate quality control and bioactive compound analysis in Ganoderma research and commercial applications.

32 Brandon Burrell

Master's student in Pharmaceutical Sciences 

Advisor(s): Timothy Vaden

College of Science and Math (CSM)

Title:  Effects of fatty acid ionic liquids on tyrosinase activity

Abstract: Fatty acid ionic liquids (FAILs) can form micelles in aqueous solution and are often non-toxic. Therefore, FAILs are potential biomaterials for protein encapsulation with applications in diverse fields. Encapsulation of enzymes by FAILs may be able to alter their biocatalytic activities. As a model system, we have studied the effects of FAILs on the enzyme tyrosinase, which oxidizes tyrosine. This presentation will show results from tyrosinase enzyme activity assays in the presence of different biocompatible FAILs in solution. Enzyme rates and rate constants are reported for assays performed when both enzyme and substrate are prepared in the presence of FAILs, and are shown to differ significantly from the values for tyrosinase in conventional physiological environments.

33 Sravani  Malasani

PhD student in Pharmaceutical Chemistry 

Advisor(s): Zhihong Wang

College of Science and Math (CSM)

Title:  Decoding the Role of Protein-Protein Interactions in MAPK Driven Cancers

Abstract: The MAPK signaling cascade involving RAS–RAF–MEK–ERK is a central regulator of cell proliferation and differentiation and is frequently dysregulated in cancer. RAF kinases are activated through interactions with GTP-bound RAS
at the plasma membrane. While RAF activation is known to involve RAS-dependent membrane recruitment and dimerization, the molecular features of RAS-RAF engagement that promote activation of the RAF kinase domain need
further Characterization. In this study, we investigated how distinct N-terminal domains of BRAF contribute to interactions with NRAS and KRAS, and how oncogenic Ras mutations and pharmacological Ras inhibitors modulate these
interactions. Using Open surface plasmon resonance (Open SPR), we characterized binding affinities between NRAS and a series of BRAF N-terminal constructs encompassing the BSR, RBD, and CRD domains. These analyses
revealed differential binding affinities across domains, indicating that multiple regions within the BRAF N-terminus contribute to NRAS engagement. We further examined interactions between oncogenic NRAS and KRAS mutants and
the BRAF N-terminal region, observing mutation-dependent differences in binding strength relative to wild-type RAS. These interactions were independently validated using GST pulldown assays. Additionally, we employed biolayer
interferometry (BLI) to assess the impact of pharmacological perturbation on RAS-RAF binding. To compliment these biophysical studies, live-cell NanoBiT assays were used to quantify RAS-RAF interactions in live cells, conforming
isoform-specific differences between NRAS and KRAS interactions with BRAF. Furthermore, molecular dynamics (MD) simulations provided structural insight into the KRAS-BRAF interface, identifying key residues that mediate
interactions between the RAS hypervariable region (HVR) of KRAS and the BRAF BSR domain. Together, these findings define key determinants of RAS–RAF interactions and demonstrate that oncogenic mutations and smallmolecule
inhibitors dynamically regulate RAS-RAF engagement, providing insight into early steps of RAF activation in RAS-driven signaling.

34 Govinda Navale

Post-Doctoral Fellow in Pharmaceutical Sciences 

Advisor(s): Zhihong Wang

College of Science and Math (CSM)

Title:  Therapeutic Targeting SHOC2-MRAS-PP1C Phosphatase Complex in RAF activation

Abstract: The Mitogen-Activated Protein Kinase (MAPK) pathway is a critical regulator of key cellular processes, including proliferation and survival, and its dysregulation is frequently associated with cancers and RASopathies. The SHOC2–MRAS–PP1C (SMP) phosphatase complex is an important modulator of RAF activation within MAPK signaling. This complex promotes RAF activation by dephosphorylating inhibitory
phosphoserine residues (ARAF S214, BRAF S365, and CRAF S259), thereby enabling RAF translocation to the plasma membrane and subsequent pathway activation. The SMP complex plays a pivotal role in RAS-driven cancers, with SHOC2 emerging as a promising therapeutic target due to its dependence on
oncogenic RAS signaling. In this study, we investigated the therapeutic potential of targeting the SMP complex using a natural product (celastrol), small molecules (SM-1 and SM-2), and designed peptides (P1, P2, and P3) inhibitors. High-throughput screening of more than 5,000 small molecules was performed using a NanoBiT assay, complemented by molecular docking, co-immunoprecipitation (Co-IP),
and Western blot analyses. These approaches identified several candidate inhibitors capable of disrupting SHOC2–MRAS interactions. The identified compounds and peptides interfered with SMP complex formation, likely through competitive binding to SHOC2 or MRAS, thereby attenuating downstream RAF activation. Collectively, these findings highlight a dual therapeutic strategy: direct disruption of the SMP complex using small molecules and modulation of protein–protein interactions through SMP-targeting peptides to suppress aberrant MAPK signaling. Ongoing studies focus on validating these inhibitors and further elucidating their mechanisms through detailed biophysical and cellular analysis.

35 Salma Afia Ratri

PhD student in Civil Engineering

Collaborator(s): Zamee, Ahmed Imtiaz

Advisor(s): Jalayer, Mohammad

Henry M Rowan College of Engineering

Title:  Enhancing Work Zone Safety by Integrating Internal Traffic Control Plan

Abstract: The National Work Zone Safety Information Clearinghouse estimated that more than 100,000 work zone crashes occurred on United States (US) highways in 2023, resulting in the deaths of 898 people and injuries to 40,170 others. Work zones pose serious safety concerns, with high rates of injuries and deaths caused by collisions involving construction vehicles and mobile equipment operating within the project area. These incidents occur more frequently than crashes caused by passenger vehicles entering work zones, highlighting the need for improved traffic control within the work zone itself. An Internal Traffic Control Plan (ITCP) can improve safety by organizing the movement of construction vehicles, equipment, and workers operating within the work zone. This study evaluated the feasibility and benefits of integrating ITCPs into construction projects of state departments through a review of established ITCP practices across the United States. The study also reviews various traffic control devices and advanced technologies related to smart work zones, including queue warning systems, dynamic message signs, changeable message signs, and vehicle-to-infrastructure (V2I) technologies. In addition, interviews were conducted with researchers, contractors, and traffic control experts from different state agencies to examine current practices, implementation and enforcement methods, and the use of advanced technologies in work zone traffic control. Based on identified best practices and implementation gaps, the study proposes a structured ITCP integration framework and provides data-driven recommendations aimed at improving worker safety, traffic coordination, and overall work zone efficiency on state Department of Transportation (DOT) construction projects.

36 Aymen Bahri

PhD student in Electrical and Computer Engineering  

Advisor(s): Islam Mantawy

Henry M Rowan College of Engineering

Title:  Bridges Point Cloud Segmentation with Graph Neural Networks

Abstract: This study investigates the effectiveness of graph neural networks (GNNs) for semantic segmentation of bridge point clouds, a key step in structural health monitoring and automated Bridge Information Modeling (BrIM).
Bridges feature complex geometries, occlusions, scale variations, and ambiguous component boundaries (e.g., girder-pier junctions), where conventional point-based networks (e.g., PointNet) or voxel-based methods often lose local details and fail to generalize across bridge types.
Various GNN-based approaches have been explored in the literature to address these challenges. They typically construct graphs from raw LiDAR or terrestrial laser scanning points, such as dynamic k-nearest-neighbor graphs, with edge attributes that may incorporate Euclidean distances and structural priors (e.g., curvature or orientation) to embed domain knowledge.
Models employ multi-scale graph convolutional layers, often combined with edge conditioned attention or similar mechanisms, to aggregate local neighborhood features. Residual message passing or related propagation techniques help capture long-range dependencies among components like decks, piers, girders, cables, and suspenders.
By exploiting the relational inductive bias of GNNs, these methods model the interconnected nature of bridge elements, improving robustness to incomplete scans and boundary ambiguities compared to generic 3D segmentation approaches.
Evaluated on datasets such as the BrPCD databank (a diverse set of real and augmented bridge scans) and additional long-span bridge data, GNN frameworks demonstrate strong capability in distinguishing bridge parts and support scalable, automated infrastructure inspection and digital modeling.
This work explores different graph constructions and operations,dynamic k-NN graphs, multi-scale convolutions, attention-based mechanisms, and residual message passing to enhance GNN performance on bridge point cloud segmentation.

37 Nicholas Paradis

PhD student in Pharmaceutical Chemistry 

Advisor(s): Chun Wu

College of Science and Math (CSM)

Title:  Near Neutral Balanced Selectionist Theory To Explain Zika Virus Evolution

Abstract: The Zika virus (ZIKV) Asian and American lineages causes severe neuroses in humans and no FDA-approved vaccine or antiviral is available. Mice model infection studies suggest the Asian and American ZIKV lineages are more fit than its older African lineage. Fitting evolutionary frameworks to genomic analysis and identifying adaptive mutations can enhance our understanding of ZIKV molecular evolution and expedite antiviral and vaccine development. We applied our novel substitution-mutation rate ratio (c/µ) framework to 884 ZIKV genomic sequences to 1) identify adaptive mutation sites altering protein phenotype (nonsynonymous protein mutations) and potentially altering protein expression (synonymous protein and regulatory mutations) and 2) to elucidate the combined interplay of natural selection and genetic drifting forces on its molecular evolution. Numerous adaptive mutations (c/µ > 1) were consistent with their reported enhanced pathogenicity and neurovirulence phenotypes in the literature. Synonymous mutations in protein and regulatory regions modulate nucleic acid structure stability and could shape codon usage and protein expression. Moreover, the ZIKV genome and genes exhibited an L-shaped c/µ distribution of fitness effects (DFE) with mixed observance of the time-independent substitution rate (molecular clock). These results are consistent with the Nearly-Neutral Selectionist Theory (NNST) instead of Kimura’s Neutral Theory (KNT) or Selectionist Theory (ST). ZIKV molecular evolution seems to be largely shaped by symmetric balancing of nearly neutral selection with episodic natural selection via adaptive mutations. Interestingly, this balanced selection mechanism in the Asian and American lineages could help explain how ZIKV maintains the less lethal neurovirulent phenotype that causes infant microcephaly, compared with the African lineage, which is more neurovirulent and often terminates the developing fetus, contributing towards an evolutionary dead-end for this virus.

38 Danita Nti

Master's student in Pharmaceutical Sciences  

College of Science and Math (CSM)

Title:  MACROCYCLIC DISULFIDE FOLDAMERS

39 Barnabas  Gao

PhD student in Chemical Engineering 

Advisor(s): Dr. Kirti Yenkie and Dr. Robert Hesketh

Henry M Rowan College of Engineering

Title:  Flushing Dynamics in Multi-Product Pipeline Systems: Model-Driven Optimization Approach

Abstract: Maintaining product integrity during multiproduct pipeline operations remains a critical challenge within the petroleum and petrochemical industries. These pipelines transport a diverse range of refined oil products with distinct physical and chemical properties, necessitating an efficient changeover process to minimize product cross-contamination and ensure product integrity [1], [2]. The flushing process often necessitates the use of large volumes of finished oil products to displace the previous oil [3]. Thus, it leads to oil downgrade, with increased operational costs. This study presents a comprehensive numerical investigation into the flushing process of miscible yet compositionally distinct oil systems, utilizing an industrial-scale pilot plant. The objective is to develop a computational model to inform an optimized flushing strategy that enhances efficiency, minimizes product loss, and ensures process reproducibility.
Two distinct flushing methodologies were examined: direct oil-to-oil flushing, in which a plug of fresh oil is introduced to displace the residual oil, and air-blowing followed by flushing, where air is initially introduced to remove most of the residual oil before introducing the flush oil. Viscosity was used as a primary performance metric, continuously monitored through an inline viscometer and a real-time data acquisition system.
A flushing model based on a simple mole balance approach was developed, coupling the pipeline system into two primary geometric components: a tubular section and a tank section. The developed model accounts for deviations from ideal behavior by the introduction of two correction parameters: alpha (α), which accounts for variations in flow behavior and their influence on displacement efficiency as well as mixing, and beta (β), which quantifies the portion of residual oil that remains within the pipeline without being displaced by the flushing fluid through a bypass. By integrating systematic experimentation with numerical modeling, this study advances understanding of flushing dynamics and provides an optimized framework for process control.
References
[1] B. Gao et al., “Improved Design of Flushing Process for Multi-Product Pipelines,” presented at the Foundations of Computer-Aided Process Design, Breckenridge, Colorado, USA, Jul. 2024, pp. 137–144. doi: 10.69997/sct.171679.
[2] S. Gao and C. Dennar, “Computational Simulation of Multi-Product Flow in an Oil Transportation Pipeline,” Appl. Mech. Mater., vol. 590, pp. 161–165, 2014, doi: 10.4028/www.scientific.net/AMM.590.161.
[3] S. S. Jerpoth, R. Hesketh, C. S. Slater, M. J. Savelski, and K. M. Yenkie, “Strategic Optimization of the Flushing Operations in Lubricant Manufacturing and Packaging Facilities,” ACS Omega, vol. 8, no. 41, pp. 38288–38300, Oct. 2023, doi: 10.1021/acsomega.3c04668.

40 Ryan Kleynowski

Master's student in Mathematics: Statistics Concentration 

Advisor(s): Nasrine Bendjilali

College of Science and Math (CSM)

Title:  Convolutional Network Optimization via Statistical Model-Based Pruning.

Abstract: Determining an effective Convolutional Neural Network (CNN) architecture is a challenging task. A common strategy is to overparameterize the model to ensure it has sufficient capacity to fit the data. However, this approach increases the risk of overfitting, which can reduce the model’s ability to generalize to unseen data. In this paper, we introduce a new approach for identifying redundant channels to detect redundancy within convolutional layers. Our objective is to mitigate overfitting by pruning unnecessary channels during the training process. This approach produces a smaller and more efficient model while reducing overfitting on the training set and maintaining performance on the validation set. Simulation studies demonstrate that pruning redundant channels preserves validation accuracy while slightly reducing training accuracy, indicating reduced overfitting with minimal impact on overall performance.

41 Ali Subhan

PhD student in Data Science 

Advisor(s): Nasrine Bendjilali

College of Science and Math (CSM)

42

Charitha

Rathnayaka

PhD student

Data Science

Collaborator(s):

Advisor(s):

Prof. Shen Shyang Ho

College of Science and Math (CSM)

Title:

Improving AI Alignment and Tool Learning in Language Models via Metacognitive Reinforcement Learning

Despite significant progress in large language models (LLMs), key challenges such as AI alignment and reliable tool learning remain unresolved. AI alignment aims to ensure that model behavior remains consistent with human intentions and preferences, while tool learning focuses on enabling models to effectively select and utilize external tools in multi-step tasks. Recent work such as StepTool models tool learning as a dynamic decision-making process and introduces a step-grained reinforcement learning framework that assigns rewards to each tool interaction and optimizes tool usage across multiple decision steps. While StepTool significantly improves multi-step tool usage, current approaches still lack mechanisms for internal self-evaluation during action selection, which is crucial for improving both alignment and decision reliability.
Inspired by human metacognition, the Metacognitive Actor-Critic (MAC) framework introduces a mechanism where candidate actions are internally evaluated before execution, allowing the system to detect potentially suboptimal decisions. Additionally, Reinforcement Learning from Human Feedback (RLHF) has been widely used to improve alignment by incorporating human preference signals during training.
In this work, we propose a combined framework integrating Metacognitive Actor-Critic (MAC), RLHF, and StepTool to address both AI alignment and tool learning challenges in LLMs. Specifically, we use MAC and RLHF to improve alignment by enabling internal evaluation of generated actions using both critic-based confidence and human preference rewards, while extending StepTool with metacognitive evaluation and RLHF signals to improve decision-making in multi-step tool usage. By combining step-grained reinforcement learning with metacognitive evaluation and human feedback, the proposed approach aims to improve alignment, reduce hallucinations, and enhance reliable tool learning in LLMs.

43

Suprova

Paul

Master's student

Data Science

Collaborator(s):

Advisor(s):

Dr. Shen-Shyang Ho

College of Science and Math (CSM)

Title:

Community-Aware Change Point Detection in Evolving Graphs using Martingale Framework

Detecting structural changes in evolving networks is an important problem in many real-world systems such as social networks, communication networks, and biological interaction networks. Most existing approaches focus on global change point detection, where the objective is to determine whether the overall network structure has changed over time. However, global detection methods often fail to identify where within the network the change occurs, especially when structural changes are localized within specific communities.
In this work, we extend the martingale-based change-point detection method for evolving graphs to support the detection of localized structural changes. Specifically, we propose a community-aware framework for monitoring and detecting structural changes in communities of an evolving graph. We present preliminary results on synthetic datasets to demonstrate the feasibility and potential effectiveness of the proposed approach.

44 Tarun Teja Kairamkonda

PhD student in Data Science 

Advisor(s): Dr. Nidhal Bouaynaya

College of Science and Math (CSM)

Title:  Not All Errors Are Equal: Contextual Error Rate for Safety-Critical Air Traffic Control ASR

Abstract: Automatic Speech Recognition (ASR) systems are typically evaluated using Word Error Rate (WER), which measures overall transcription accuracy. However, in safety-critical domains such as Air Traffic Control (ATC), not all words carry equal importance. Errors involving critical contextual information—such as callsigns, flight levels, runway numbers, and navigation commands—can have significantly greater operational consequences than other transcription errors. As a result, WER alone may not adequately reflect the reliability of ASR systems in such environments.

In this work, we investigate the use of Contextual Error Rate (CER) as a complementary evaluation metric that focuses specifically on domain-critical tokens. We evaluate several large-scale ASR models, including Whisper and NVIDIA Parakeet variants, on multiple ATC speech datasets. Models are adapted to the ATC domain using transfer learning and parameter-efficient fine-tuning techniques, and their performance is evaluated across both in-domain ATC datasets and general-domain speech benchmarks.

Our results show that while fine-tuning substantially improves WER on ATC datasets, models with similar WER can exhibit significantly different contextual error rates. This highlights that WER may overestimate ASR reliability in safety-critical communication, whereas CER provides a more informative measure of system performance with respect to operationally important information. These findings suggest that context-aware evaluation metrics are essential for assessing ASR systems deployed in safety-critical environments.

45 Giuseppina Carannante

Post-Doctoral Fellow in MAVRC 

Advisor(s): Nidhal Bouaynaya

Henry M Rowan College of Engineering

Title:  Uncovering Hidden Risks in Aircraft Final Approaches Using Data-Driven Anomaly Detection

Abstract: Despite continuous improvements in aviation safety, a significant proportion of accidents and incidents still occur during the final approach phase of flight. With the growing demand for air mobility, the Federal Aviation Administration (FAA) and the aviation industry have emphasized the need for proactive risk management. This involves identifying vulnerabilities and assessing unknown risks ("unknown unknowns") or accident precursors to mitigate potential hazards before they escalate into incidents. Traditional Flight Operational Quality Assurance (FOQA) approaches rely on predefined exceedance thresholds and watch lists, which are effective for monitoring known risks but limited in detecting previously unknown anomalies in complex flight data.
This work presents a data-driven framework for detecting anomalous aircraft behavior during final approach using Temporal Fusion Transformers (TFT). Unlike commonly used anomaly detection approaches based on autoencoders or variational autoencoders, the proposed method reformulates anomaly detection as a trajectory prediction problem. The TFT architecture integrates both static contextual information, such as airport and runway characteristics, and dynamic flight variables to capture temporal dependencies and operational context within aircraft trajectories.
Anomalies are identified through prediction errors between observed and predicted trajectories, enabling the detection of deviations from typical flight behavior. The TFT model also provides interpretable insights into the factors contributing to anomalous predictions, supporting more transparent safety analysis.
Experimental evaluation using real-world flight trajectory data demonstrates that the proposed approach improves anomaly detection performance compared with autoencoder-based methods, reducing false positives while maintaining high sensitivity to safety-critical deviations. These results highlight the potential of transformer-based architectures to support scalable, interpretable, and proactive identification of operational vulnerabilities in aviation, contributing to enhanced safety monitoring in increasingly complex air traffic environments.

46 Hoi Yan Yu

PhD student in Biomedical Engineering 

Advisor(s): Sophia Orbach

Henry M Rowan College of Engineering

Title:  Personalized Machine Learning Model for Liver Toxicity in Breast Cancer Patients

Abstract: Drug-induced liver injury (DILI) is a frequent and serious adverse effect in cancer patients undergoing systemic treatment. The onset of DILI can delay therapy, lead to hospitalization, and in severe cases, become life-threatening. Early identification of patients at an elevated risk for liver toxicity remains a clinical challenge, particularly due to the heterogeneous nature of cancer populations and the complexity of individual biological responses. Biomarkers such as alanine aminotransferase (ALT) are routinely used to assess liver damage or disease, but are not sufficient to predict the risk of toxicity prior to the development of organ damage.
This study aims to develop a machine learning-based classifier that predicts liver toxicity risk in cancer patients using clinical and demographic data. This classifier integrates various levels of patient data as inputs, including blood-based measurement values, demographic variables, and genetic polymorphisms. The goal is to explore the feasibility and limitations of predictive modeling using secondary sources of patient data to support early detection and prevention of liver-related complications in cancer treatment.

47 Maria Lentini

PhD student in Mathematics: Statistics Concentration 

Advisor(s): Umashanger Thayasivam

College of Science and Math (CSM)

Title:  Mixture of Latent Attributes (MoLA): Learning Low-Dimensional Learner Profiles in High-Dimensional Attribute Spaces

Abstract: Modeling learner proficiency in high-dimensional curricula remains a fundamental challenge in educational data mining. Traditional Cognitive Diagnostic Models (CDMs) offer interpretable, skill-level feedback but are paralyzed by an exponential computational bottleneck ($2^K$) as the number of attributes increases. We introduce the \textbf{Mixture of Latent Attributes (MoLA)}, a transformative framework that shatters this complexity barrier. By modeling proficiency as a sparse mixture of representative cognitive archetypes, MoLA achieves linear scalability $O(MK)$, enabling granular diagnosis in massive datasets where traditional models fail.

Evaluating on multiple high-dimensional datasets and the fraction-subtraction dataset, we demonstrate that MoLA achieves competitive predictive accuracy while remaining computationally efficient and diagnostically superior, offering a scalable and interpretable path for personalized education at scale.

48 Attanasia Garuso

Master's student in Computer Science 

Advisor(s): Silvija Kokalj-Filipovic

College of Science and Math (CSM)

Title:  Vector Quantized Information Bottleneck Mitigation of Adversarial Attacks on Modulation Classifiers

Abstract: Radio-frequency (RF) signal processing based on machine learning, including Automatic Modulation Classification (AMC), plays a critical role in Next Generation (NextG) intelligent communications but remains highly vulnerable to adversarial attacks. While prior work has explored Variational Information Bottleneck (VIB) as a means to improve robustness  in continuous latent spaces, its application to  RF classifiers with discrete latent representations remains unexplored. We therefore investigate the impact of discrete-space information bottlenecks on the adversarial robustness of AMC models. Classification accuracy on adversarially attacked RF waveforms is evaluated using two VIB-based frameworks: (i) a two-stage framework where VIB is applied in the preprocessing step using a Vector Quantized Variational Autoencoder (VQVAE) to suppress adversarial perturbations in the process of input signal reconstruction and (ii) a Vector Quantized Classifier (VQC) that leverages VIB inside the classifier proper. The  architectures of VQVAE and VQC are identical up to the bottleneck where the VQVAE adds a decoder while VQC utilizes a classification head. We train both VQVAE and VQC  with loss functions that we derive from the VIB information-theoretic model. Both frameworks incorporate vector quantization and KL regularization to enforce discrete bottlenecks that filter out adversarial noise. Evaluations under white-box attacks demonstrate that discrete bottlenecks significantly enhance robustness: VQVAE outperforms its continuous VAE (Variational Autoencoder) counterpart, while VQC achieves strong robustness without requiring signal reconstruction.

49 Wen Zhou

PhD student in Computer Science 

Advisor(s): Sihan Yu

College of Science and Math (CSM)

Title:  Rebot: A Code Agent for End-to-End Software Project Generation

Abstract: The development of LLMs and intelligent software engineering is transforming code generation workloads into end-to-end software project generation processes. Their efficient execution is hindered by unstable reasoning behavior arising from long-context reasoning and the complex dependency topologies of production-grade software projects. Existing approaches often address this challenge by extending prompt engineering techniques or employing multi-agent workflows.

In this paper, we argue for a more fundamental solution and propose a topology-aware generation paradigm that models software projects as generation graphs driven by structural dependencies and represents the execution of a Code Agent as a sequence of code generation and repair operations progressing along the project topology. Our key contributions include:
(1) a project-level code generation model that maintains consistency and composability under multi-code dependency structures;
(2) native reasoning stabilization mechanisms that preserve reasoning stability without unbounded context expansion; and
(3) a topology graph–based execution architecture for software generation, featuring WaveCoder wave scheduling, real-time symbol synchronization, and container-aware visual analysis, enabling UI quality assessment without requiring a live interface.

Under a conservative single-machine evaluation, our prototype system automatically generates runnable software-level applications, validating the efficiency of the paradigm and providing a new blueprint for software engineering–grade code agents.

50 Amine Khelifi

PhD student in Data Science 

Advisor(s): Nidhal Bouaynaya

College of Science and Math (CSM)

Title:  AI-Driven Solutions For Aviation Safety

Abstract: Rotorcraft operations face significantly higher accident rates than fixed-wing aviation, often due to incomplete infrastructure data, undetected environmental hazards, and limited in-flight monitoring capabilities. This work presents a suite of AI-driven solutions designed to enhance aviation safety through scalable, cost-effective deep learning technologies. First, we develop computer vision frameworks for automatic detection and localization of helipads and runways from high-resolution satellite imagery, addressing gaps in outdated Federal Aviation Administration (FAA) landing zone databases. Using Mask R-CNN for helipad segmentation and Faster R-CNN for runway detection, the models achieve accuracies of up to 94% and 90%, respectively. Second, we introduce a zero-shot vision-language approach for identifying obstacles surrounding helipads—such as trees, poles, and antennas—using natural language prompts, enabling adaptable hazard detection across diverse environments without task-specific retraining. Finally, we propose a deep learning-based flight data monitoring system that analyzes cockpit video to automate helicopter classification, gauge detection, and instrument reading estimation, providing a low-cost alternative to traditional flight data recorders. Together, these methods improve situational awareness, infrastructure accuracy, and operational monitoring for rotorcraft and fixed-wing aviation. The proposed solutions demonstrate strong performance across multiple datasets and environments, contributing scalable AI tools for safer and more resilient aviation operations.

51 Md Sadman Islam

PhD student

Civil Engineering

Advisor(s):Mohammad Jalayer

Henry M Rowan College of Engineering

Title: Evaluation of Pavement Markings and Guideline Development for New Jersey

Abstract: Pavement markings are critical for guiding drivers, enhancing roadway safety, and supporting Advanced Driver Assistance Systems (ADAS) and automated vehicle technologies. Their effectiveness relies on consistent visibility under all conditions, yet traditional measures like retroreflectivity primarily capture nighttime performance and overlook key factors such as pavement contrast, marking width, and daytime visibility. Degradation is often accelerated by weather, traffic, poor surface preparation, and suboptimal application, sometimes resulting in premature failure. This study conducted a comprehensive evaluation of various pavement marking materials across New Jersey, using three years of video data collected statewide. Machine learning techniques were applied to classify marking conditions and assess performance trends over time, enabling a data-driven understanding of durability and visibility. A before–and–after analysis was also performed to examine the effectiveness of materials and installation practices. Moreover, expert interviews provided valuable insights to establish threshold values for retroreflectivity and other performance indicators suited to New Jersey’s roadway. Based on these findings, the study proposes updated guidelines, recommended thresholds, use of materials, and alternative testing protocols for quick deployment during supply chain disruptions. The results aim to improve installation standards for materials used, support proactive maintenance strategies, and ensure markings remain visible and effective for both human drivers and automated vehicle systems under real-world operating conditions.

52 Lu Lin

PhD student in Data Science  

College of Science and Math (CSM)

Title: On Language Learnability: From String Patterns to Oblivious Algorithm

53 Zubair Hafeez

PhD student in Data Science 

Advisor(s): Silvija Kokalj-Filipovic

College of Science and Math (CSM)

Title:  Spatial Graph Embedding and Multimodal Alignment for Predictive RF Propagation in FoF Scenes

Abstract: NextG's demands for ultralow-latency, low-overhead channel estimation are driving interest in predictive RF generative AI (GenAI), which provides strong channel priors for fast Bayesian estimates. In factory-of-the-future (FoF) contexts — characterized by dense mobile wireless scenarios and severe electromagnetic interference — we propose SceneSense, a GenAI agentic framework that adapts channel priors through multimodal prompts (text, drawings, LiDAR point clouds, or photos) to enable real-time wireless channel simulation and prediction. SceneSense addresses the computational burden of physics-based tools like NVIDIA's Sionna, which uses ray-tracing through Blender-modeled 3D scenes to compute channel impulse responses (CIRs) for transmitter-receiver pairs — a process that becomes especially demanding in time-varying Massive MIMO scenarios. By training on Sionna-generated estimates paired with matching multimodal scene descriptions, SceneSense acts as a real-time digital twin of Sionna, with a proof-of-concept already demonstrating latency reductions of 5–11 milliseconds and a design path toward sub-2-millisecond CIR prediction, supporting Massive MIMO and fast-moving object tracking with substantial computational gains over both traditional channel estimation and direct simulation.

54 Jamael Ajah

PhD student in Experiential Engineering Education 

Advisor(s): Cassandra Jamison

Henry M Rowan College of Engineering

Title:  Understanding How Engineering Students Use GenAI and the Academic and Career Motivations Behind Their Use

Abstract: The rapid proliferation of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT and Microsoft Copilot, creates a unique challenge in engineering education. These tools are widely accessible, capable of producing helpful information to support learning, yet equally capable of generating confident-sounding but fabricated content. Prior research shows that engineering programs are high-pressure environments where competitive pressures can shift students' priorities from deep learning to academic performance metrics like GPA. In light of these tensions in engineering education, it is important to ask: how are engineering students actually using GenAI, and what academic and career motivations shape that use?
This mixed-methods study investigates the use of GenAI among undergraduate engineering students at Rowan University. It focuses on three areas: (1) how they use GenAI, their academic motivations, and career motivations influencing their use. We integrate the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the Attitude construct. Students’ usage patterns will be classified according to Anthropic's empirically derived taxonomy of student-AI interaction patterns. We will quantitatively collect demographic and academic background information, self-reported usage patterns, and students' perceptions across the UTAUT2 constructs. Qualitatively, both the UTAUT2 constructs and usage patterns will guide semi-structured interviews to explore how engineering students' academic goals and career aspirations shape their decisions about when, how, and why they use GenAI. As the work is in its formative stage, the poster will feature a literature review that motivated the work and the development of the survey and interview protocols.
The long-term findings of this work are anticipated to contribute to a typology that links engineering students' academic and career motivations to their interactions with GenAI. In practice, this framework could potentially inform the design of acceptable use policies and help align GenAI-integrated assignments with the intended learning outcomes at Rowan and similar institutions.

55 Jayanto Das

PhD student in Mechanical Engineering 

Advisor(s): Paromita Nath

Henry M Rowan College of Engineering

Title:  Contextualizing Student Perspectives on Asynchronous Online Learning through the Community of Inquiry Framework

Abstract: Online learning environments often rely on digital traces of student activity, such as learning analytics data, to infer engagement, yet such behavioral indicators rarely capture the motivations, challenges, and contextual factors that shape how students experience online courses. This study utilizes student perspectives to contextualize and understand online learning through the Community of Inquiry (CoI) framework. Using a hybrid human–large language model (LLM) methodology, semi-structured interviews with 30 undergraduate engineering students enrolled in asynchronous online courses were analyzed. Human coders and an LLM independently extracted themes related to the cognitive, social, and teaching presences of the CoI framework, enabling a comparative analysis of structured manual coding and automated thematic extraction. The findings indicate that students employ learning strategies such as selective video engagement, offline note-taking, and learning environment management that are not reflected in standard engagement metrics. Social presence was constrained by the asynchronous format, with discussion boards frequently perceived as perfunctory. Teaching presence emerged as a critical determinant of engagement as students distinguished meaningful rigor from “busy work” and invested time when instructional quality was high, regardless of workload. These insights emphasize that strengthening presence is essential for creating effective asynchronous online learning experiences.

56 Tristan Letizia

PhD student in Experiential Engineering Education

Collaborator(s): Rebecca Sprier

Advisor(s): Sarah Wilson

Henry M Rowan College of Engineering

Title:  Using Large Language Models to Identify Core Competencies in Entry-Level Chemical and Mechanical Engineering Job Postings

Abstract: Background: This work-in-progress research paper investigates how large language models can be used to identify core engineering competencies in entry-level job postings and how these results compare to traditional qualitative analysis approaches. Engineering students often report uncertainty and lack of confidence when navigating the job application process, in part because curricula and professional preparation activities are not always explicitly aligned with employer expectations. Job postings represent a rich but underutilized data source for understanding workforce expectations across engineering disciplines. Recent advances in large language models enable scalable analysis of large text-based datasets; however, their effectiveness and validity for engineering education research must be systematically examined.
Purpose/Research Questions: The purpose of this study is to identify core competencies emphasized in entry-level chemical and mechanical engineering job postings and to evaluate the use of large language models for this task. The study is guided by three research questions: (1) How can large language models be used to identify core engineering competencies in chemical and mechanical engineering job postings? (2) How does competency identification using large language models compare to deductive qualitative thematic analysis conducted by researchers? and (3) To what extent do emphasized competencies and their application differ between chemical and mechanical engineering entry-level positions?
Methods: This study will analyze 100 entry-level job postings, including 50 chemical engineering and 50 mechanical engineering positions, sourced from LinkedIn Jobs. Competencies will be coded using a deductive framework based on the 16 core engineering competencies identified by Passow and Passow in their 2017 systematic review. Two parallel analyses will be conducted. First, researchers will apply deductive qualitative coding to identify competencies represented in each posting, followed by inductive thematic analysis of competencies that do not align with the existing framework. Second, an iterative large language model-based coding approach will be developed to classify competencies using the same framework. Agreement between the large language model and researcher coding outputs will be assessed using inter-rater reliability metrics, which will be used to refine the large language model prompting and analysis strategy.
Findings: As a work-in-progress, findings are forthcoming. We anticipate substantial agreement between large language model and researcher coding at the competency level, with opportunities to improve alignment through iterative refinement. While major differences in the presence of core competencies across disciplines are not expected, we anticipate disciplinary variation in how these competencies are described and operationalized within job postings.
Implications: This work contributes methodological insight into the use of large language models for engineering education research and provides a scalable approach for analyzing workforce expectations. Findings have the potential to inform discipline-specific curricular design, professional development programming, and career preparation efforts that better align student training with employer needs. Future work will expand the dataset and refine validation strategies.

57 Jacqueline Tawney

Post-Doctoral Fellow in Experiential Engineering Education 

Advisor(s): Cassandra Jamison

Henry M Rowan College of Engineering

Title:

Abstract: Educators are being called to prepare future engineers with the sustainability competencies needed to “meet the needs of the present without compromising the ability of future generations to meet their own needs” (Brundtland, 1987). Yet sustainability‑focused curricular changes are often faculty‑driven, which risks overlooking student agency. Here, agency refers to students’ ability to understand their own needs, shape their own learning, and take action with others to create change—capacities inseparable from sustainability work, which requires recognizing personal, relational, and systemic needs while collaborating to create just and sustainable solutions.

This project addresses this gap by co‑creating a sustainability‑focused summer program with and for engineering undergraduates using a participatory and critically informed approach. Grounded in Participatory Action Learning and Action Research (PALAR), students serve as collaborators whose insights guide program goals, structure, content, and inquiry. PALAR provides an overarching framework that informs both our learning environment and our study design, promoting students and faculty to learn, act, reflect, and build alongside each other during the program’s development and implementation. This approach is especially well-suited for sustainability education given “its emphasis on community collaboration, its focus on creating practical solutions to pressing problems, and its ability to work on complex issues with high levels of uncertainty through iterative processes of action and reflection” (Brydon-Miller, 2024).

During AY 2026–27, multidisciplinary capstone design teams will analyze sustainability frameworks (e.g., Engineering for One Planet), facilitate focus groups with stakeholders, develop criteria for an effective sustainability program, and help design program components. The program will launch in Summer 2027 with up to 24 undergraduates from engineering and related STEM fields. The study will examine (1) the value co‑creation adds to program design; (2) the resulting program’s impact on student‑designers’ and participants’ agency and sustainability competencies; and (3) an open‑ended, student‑led research question.

58 Diane Duncan

PhD student in Equity, Access, and Success in Education  

College of Education

59 Court Shuller

Master's student in Reading Education  

College of Education

Title: Fellowship in Focus: Creating Accessible Professional Learning Through Video

Abstract: This poster describes participation in the Goyen Literacy Fellowship, a program that supports educators in professional teaching and learning based in reading science. During the fellowship, participants document their practice through videos and blog posts, present one public facing professional learning session, and attend monthly meetings to learn about different evidence based practices.

Not only does the Fellowship provide a structure and setting for collaborative professional learning, it also provides fellows with access to an array of professional networking opportunities that continue to emphasize its core idea of using classroom video for reflective and effective professional development. This underground network of educators actively work to improve themselves, and their field. Opportunities tied to the Fellowship network included publishing content to The Teachers Table, as well as participating in interviews for the blog Voices from the Field, and The Road to Reading Podcast. Additionally, Fellows piloted and delivered a customized professional development protocol called Teachers Teach Teachers for a Nevada charter school.

The culminating Fellowship project that resulted is Science of Reading Snaps, a social media account that aims to connect real educators with bite sized professional development aligned to the Science of Reading. In a culture where teachers are at capacity, short form vertical content is an engaging and accessible way to bridge theory to practice. These one to four minute videos contain quick explanations of learning science strategies, and video of these practices being used in a real classroom. Video topics posted include outside-in vocabulary, developing word consciousness, self-regulated strategy development, fluency, socratic seminar, curriculum pacing, modeling and more. With over 30 videos in just six months and more than 75,000 views, Science of Reading Snaps success shows how short, classroom centered content can engage educators, bridge theory and practice, and support sustained professional learning.

60 Rebecca Spirer

PhD student in Experiential Engineering Education

Collaborator(s): Letizia, Tristan

Advisor(s): Cassandra Jamison

Henry M Rowan College of Engineering

Title: Using Large Language Models to Identify Core Competencies in Entry-Level Chemical and Mechanical Engineering Job Postings

Abstract: We investigated how Generative AI (GenAI) can be used to identify core engineering competencies in entry-level job postings and how these results compare to traditional qualitative analysis approaches. Job postings represent a rich potential data source for understanding workforce expectations across engineering disciplines. Simultaneously, recent advances in GenAI show promise for scalable analysis of large text-based datasets. The purpose of this study is to identify core competencies emphasized in entry-level chemical (ChE) and mechanical (ME) engineering job postings and to evaluate the use of GenAI for this task. We analyzed 50 entry-level job postings, including 25 ChE and 25 ME positions, sourced from LinkedIn Jobs. Competencies were coded using a deductive framework based on the 16 core engineering competencies identified in Passow and Passow’s 2017 systematic review. Two parallel analyses were conducted - one by human researchers and one by GenAI. Qualitative coding of the job postings was conducted by the researchers using MAXQDA. Findings demonstrated that for both ChE and ME job postings, the following competencies were the most prevalent: apply knowledge, apply skills, communicate effectively, and coordinate efforts. Researchers then developed a structured prompt for ChatGPT aimed at coding job postings and minimizing hallucinations. Job postings previously coded by researchers were input and coded by ChatGPT based on the competencies descriptions. This project will present the initial results related to the agreement between the GenAI and researcher coding outputs using inter-rater reliability metrics, which will inform suggestions for how to refine the GenAI prompting and analysis strategy. Initial findings demonstrate promising similarities between the coding of human researchers and GenAI. This work contributes methodological insight into the use of GenAI for engineering education research and provides a scalable approach for analyzing workforce expectations to inform discipline-specific curricular design, professional development programming, and career preparation efforts that better align student training with employer needs.

61 Heather Malino

PhD student in Experiential Engineering Education 

Advisor(s): Cassie Jamison

Henry M Rowan College of Engineering

Title: Cognitive Diversity in Action: How Neurodivergent Students Engage in Engineering Estimation

Abstract: Engineering problem-solving often requires judgment in the face of uncertainty, incomplete information, and competing constraints. While prior research has examined how undergraduate engineering students develop modeling judgment, little is known about how neurodivergent students experience and exercise judgment in open-ended engineering contexts. This study is grounded in a strengths-based neurodiversity paradigm and will investigate how neurodivergent undergraduate engineering students enact emerging engineering modeling judgment (EMJ) while performing an engineering estimation task.
This qualitative study explores four dimensions of emerging judgment in engineering estimation tasks in authentic engineering contexts. It will investigate the four areas of EMJ: (1) making assumptions, (2) assessing the reasonableness of results, (3) overriding or adjusting calculated answers, and (4) deciding what resources or tools to use and when. Data will be collected through think-aloud problem-solving protocols, semi-structured interviews, and participant reflections, allowing for in-depth examination of students’ experiences and reasoning processes. The study is currently in the final stages of IRB approval, with data collection beginning shortly thereafter. Multiple data sources are used and triangulated, and analytic memoing and member checking are used to support trustworthiness.
This work challenges deficit-based narratives in engineering education and expands conceptions of competence in engineering problem-solving by centering neurodivergent students’ perspectives and strengths. Findings aim to illuminate how cognitive diversity shapes judgment practices and to inform more inclusive instructional and assessment designs that recognize diverse ways of engaging with uncertainty and estimation tasks in engineering education.

62 Joseph Midiri

Master's student in Mechanical Engineering 

Advisor(s): Dr. Cassandra Jamison

Henry M Rowan College of Engineering

Title:  Exploring the Relationship between Design Experience and Reflection in Undergraduate Engineering Students

Abstract: Reflection is a valuable tool for increasing student awareness, and reflective skill growth is now something educators are commonly seeking to build into engineering curricula. Not only are heightened reflective skills correlated with increased performance and awareness, but they play an important role in the learning process for both students and working professionals alike. However, many examples of pedagogical initiatives to teach reflective practice in engineering are limited in their scope and detached from real-world, hands-on design experiences that are typical of design-based learning contexts. This research seeks to explore this area of learning and investigate the relationship between student design experiences and reflective practice. To accomplish this, undergraduate engineering students within design groups at our institution were assigned weekly structured journals. These journals were designed to capture students’ design and reflective practices, as well as facilitate greater levels of reflectivity within their design process. Qualitative data analysis will be performed on these journal responses using deductive coding based on pre-established frameworks. Three separate frameworks are used for the coding process; one for identifying the nature of the design problem, one for describing the elements of design-based learning present, and one for reflection. This analysis is anticipated to highlight the correlations between reflection and design as described by students. Through this process, this research seeks to enable a greater understanding of these correlations as well as investigate how a reflective intervention within a design project influences the reflective practices of students. By exploring reflection within design-based learning contexts, this research has the potential to allow engineering educators to be better suited to cultivate reflective mindsets within engineering students.

63 Jillian Peslak

Master's student in Civil Engineering 

Advisor(s): Dr. Jagadish Torlapati

Henry M Rowan College of Engineering

Title:  Integrating Oil Fate Modeling and Environmental Risk Assessment to Inform Arctic Spill Response

Abstract: As global oil production increases, oil wellhead blowouts are becoming an increasing concern in the Arctic region. Wellhead blowouts and recovery in Arctic environments pose increased risk due to cold temperatures, ice, remote areas, and sensitive ecosystems; therefore, specialized models and response strategies are essential for disaster response and spill management in vulnerable areas. Intentional wellhead ignition (IWI), the process of ingiting a blown out wellhead to burn off the oil rather than allowing it to spill into the environment, may be a suitable alternative to traditional recovery methods. IWI as a response strategy may be uniquely adapted in Arctic regions during ice seasons to reduce environmental impacts, but a framework model is needed to assess the risk associated with IWI. To develop this framework, we compared various mitigation strategies using existing tools such as the General NOAA Oil Modeling Environment (GNOME), and Arctic Environmental Response Management Application (ERMA). GNOME can simulate oil spill trajectories and recovery methods, and Arctic ERMA is a centralized online mapping tool that compiles valuable information such as daily ice concentrations and environmentally sensitive areas from the NOAA Environmental Sensitivity Index (ESI). Arctic ERMA was combined with a Valued Ecosystem Components (VECs) model to quantify environmental impacts due to oil spills and exposure to by-products during spill remediation.  This was accomplished by utilizing the data layers and sublayers present within the Arctic ERMA application using the oil spill volume and areas affected by the deposition of oil on the ecosystems and communities in Alaska. This allows us to identify areas of concern and assess the impact of oil spills on different ecosystems to propose a response strategy that can minimize the impact on environmentally sensitive areas in the Arctic region.

64 Andres David Castellar Freile

PhD student in Chemical Engineering 

Advisor(s): Kirti Yenkie

Henry M Rowan College of Engineering

Title: Synthesizing Reliable Wastewater Transportation Networks through Machine Learning and Graph-Theoretic Optimization

Abstract: The improper management of water resources remains a critical challenge for global sustainability. Despite the United Nations’ Sustainable Development Goal 6 (SDG 6), only about 56% of domestic wastewater is safely treated worldwide, and projections indicate that these targets may not be met before 2050. This underscores the urgent need for effective wastewater treatment systems to safeguard public health and the environment. Municipal wastewater treatment networks (WWTNs) consist of interconnected pipelines, pumping stations, and treatment units that convey wastewater from urban areas to treatment facilities for safe discharge. While conventional WWTN design approaches primarily focus on minimizing costs, they often neglect system reliability, which is the probability that the network performs its intended function despite component failures. Insufficient reliability can cause service interruptions, untreated discharges, and increased environmental risks. Traditional reliability assessment methods typically rely on historical failure records and expert judgment, which may be unavailable, subjective, or costly.
In this study, we present an integrated synthesis framework that combines machine learning (ML)-based reliability estimation with graph theory-based optimization for WWTN design. ML models, including eXtreme Gradient Boosting (XGBoost) and neural networks, were trained using operational data from a municipal wastewater facility to predict pipeline reliability based on features such as length, material, and installation year. This enables rapid, cost-effective, and data-driven reliability assessments. These reliability estimates are incorporated into a graph-theoretic optimization framework to generate the n-best network configurations that simultaneously optimize cost and reliability.
By embedding ML-derived reliability indicators directly into the network synthesis process, the proposed methodology advances beyond conventional cost-focused designs and traditional reliability evaluation. It offers a scalable, transparent, and robust approach for designing and assessing WWTNs, addressing both economic and operational objectives, and supporting sustainable urban water management.

65 Harriet Dufie Appiah

PhD student in Chemical Engineering 

Advisor(s): Kirti Yenkie

Henry M Rowan College of Engineering

Title:  A Machine Learning and Data Driven Holistic LCA Approach for Early-Stage Sustainability Assessment

Abstract: Global warming has intensified in recent years, resulting in serious environmental impacts such as extreme weather events, rising sea levels, biodiversity loss, and increasing wildfires. These impacts endanger global sustainability, making it critical to adopt solutions that reduce environmental harm. Among available sustainability evaluation tools, Life Cycle Assessment (LCA) has emerged as a popular method providing systematic assessment of environmental impacts over a product's whole life cycle, from raw material extraction to disposal and recycling. However, traditional LCA is primarily retrospective, relying on historical data, making it less applicable to emerging technologies still in development.
This study develops a machine learning (ML) and data analytics approach to assess the environmental impact of chemicals at the early design stage across their entire life cycle. The first phase focuses on a cradle-to-gate LCA framework, employing supervised learning models, Artificial Neural Networks (ANN) and eXtreme Gradient Boosting (XGBoost), to forecast four key Life Cycle Impact Assessment (LCIA) metrics: Resource Utilization Impact, Ecosystem Quality Impact, Global Warming Potential (GWP), and Human Health Impact. These models estimate environmental impacts for novel chemicals using thermodynamic and molecular descriptor properties.
The analysis extends to the gate-to-gate phase, evaluating environmental impact during industrial operations. Given the high energy consumption of separation technologies and their direct impact on emissions and waste, this study prioritizes their gate-to-gate assessment. A regression model based on a scaling equation estimates GWP using fundamental process parameters on an industrial scale, trained on data from literature, industrial reports, and computer simulations. Open-source AI tools assisted in data collection, with each source carefully verified for accuracy.
To demonstrate practical effectiveness, a case study is analyzed, with model-predicted GWP values validated against SimaPro LCA results.

66 Evans Eleezar

Master's student in Chemical Engineering 

Advisor(s): Jagadish Torlapati

Henry M Rowan College of Engineering

Title:  Development of a Lab-Scale Microbial Fuel Cell for In-Situ Electro-Kinetic Soil Remediation of Metals in Cold Regions

Abstract: Heavy metal contamination of the environment in the Alaskan region has been on the rise primarily due to mining and other human activities. These metals can be released into the environment due to the thawing of the permafrost during summer, allowing their flow into nearby streams and water bodies. This release of metals can contaminate the food chain and poses a significant health risk to the human population. Therefore, there is an urgent need for remediation strategies that can be adapted for cold regions and reduce their impact on the environment and human health. In this study, a three-chamber laboratory-scale microbial fuel cell (MFC) was designed and applied for the electrokinetic removal of lead (Pb) from contaminated soils under Arctic conditions. The constructed MFC was initially tested with clean soil to establish baseline electrochemical performance. Pb-contaminated soil was then treated at ambient (25 °C) and cold (5 °C) temperatures. Multiple electrode configurations were evaluated to assess their influence on electrokinetic transport and remediation efficiency. Results confirmed detectable Pb migration and removal from the soil matrix with no significant loss of efficacy at low temperature, demonstrating the robustness of the system under cold conditions. Ongoing work is assessing the potential of the system to remediate arsenic, and experimental data are being used to develop a predictive model to determine optimal operating parameters for the MFC-EKR system with a focus on improving efficiency in cold climates. Future work will extend this research to intermediate-scale systems to evaluate scalability and design considerations under field-relevant conditions. This research demonstrates the potential of MFC-based electrokinetic remediation as a sustainable technology for heavy metal removal in cold regions while simultaneously generating electricity.

67 Eliandre Russel Vistal

Master's student in Civil Engineering 

Advisor(s): Jagadish Torlapati, Ph.D.

Henry M Rowan College of Engineering

Title:  Biofuel and Biogas Recovery from Aerobically Digested Food Waste Using Hydrothermal Liquefaction and Anaerobic Digestion

Abstract: Food waste (FW) is the most frequently landfilled and incinerated material in the United States. Its disposal contributes to greenhouse gas emissions, groundwater contamination, and health burdens in overburdened communities. On-site aerobic biodigesters are increasingly marketed to large generators as a convenient pre-processing technology. These units liquefy FW within 24 hours and discharge the effluent to the sanitary sewer, which shifts organic loading from solid waste systems to water resource recovery facilities (WRRFs). However, there is limited independent data on how energy-rich these effluents are and whether they should be viewed as high-strength wastes that can support resource recovery rather than simply additional load on WRRFs.

This study investigates the potential of biodigester effluent derived from representative food waste mixtures as a feedstock for biofuel production via hydrothermal liquefaction (HTL) and biogas production via anaerobic digestion (AD). Laboratory-prepared food waste was formulated from high-loss commodities identified in USDA loss-adjusted food availability data, with separate fruit and vegetable waste (FVW), meat waste (MW), and mixed blends. Selected mixtures underwent aerobic digestion, and the resulting effluents were processed in batch HTL and AD experiments.

Results show that aerobic digestion does not eliminate the energy value of FW. Biodigester effluents retain substantial volatile solids and can generate both biocrude and methane. Compared with raw FW, biocrude yields from HTL were significantly lower, whereas biogas production from AD was generally comparable, depending on feed composition. Meat-rich feeds generate the highest biocrude and methane potentials, while fruit- and vegetable-rich feeds exhibit faster but less extensive conversion. Overall, the work demonstrates that aerobically digested food waste should be viewed not as a terminal waste stream but as a material that still has potential for renewable energy recovery.

68 Mahmuda Akter Banna

Master's student in Civil Engineering 

Advisor(s): Dr. Jagadish Torlapati

Henry M Rowan College of Engineering

Title:  An Assessment of Crude oil Bioremediation in Cold Climates using Mesophilic Bacterial Strain

Abstract: Crude oil is a complex combination of aliphatic and aromatic hydrocarbons that is regularly discharged into terrestrial habitats, due to operational failures, oil spills and leaks during the extraction of petroleum and transportation is causing extensive environmental pollution in Arctic regions. Bioremediation of hydrocarbons has shown very slowly in the cold regions due to low temperatures and lower contents of water, nutrients and organic matter. To investigate constraints, bioremediation using hydrocarbon degrading bacterium like Pseudomonas aeruginosa to assess the extent of degradation at two different temperatures at 25 °C and 5 °C. Each sample cell was amended with 10% (w/w) crude oil to simulate realistic contamination conditions in cold regions. For hydrocarbons like polyaromatic hydrocarbons (PAHs) and total petroleum hydrocarbons (TPHs) analysis were performed using gas chromatography-flame ionization detection (GC-FID). Biotic samples exhibited a notably higher TPH removal efficiency of 78% at 25 °C, confirming the strong influence of temperature on microbial degradation. Nutrient analysis using ion chromatography showed clear differences in nitrate and phosphate consumption at varying temperatures, highlighting their importance in sustaining microbial activity in cold conditions. Overall, the findings demonstrate that temperature and nutrient availability are key determinants of hydrocarbon biodegradation efficiency. The findings of this study allowed us to predict the essential insights into the efficacy of bacterial activities for enhancing bioremediation efforts in cold regions impacted by petroleum pollution.
Keywords: Crude oil, Bioremediation, Cold regions, Microbial activity, Mesophilic Bacteria

69 Marina Kim

PhD student in Chemical Engineering 

Advisor(s): Xiaohui Xu

Henry M Rowan College of Engineering

Title: Deep Eutectic Solvent Hydrogels

Abstract: Conductive soft materials are increasingly important for applications in flexible electronics, wearable devices, and energy storage systems. However, many conventional ionic gels exhibit high ionic conductivity but suffer from poor mechanical stability, limiting their practical use. Deep eutectic solvent (DES) based gels have emerged as a promising alternative due to their tunable physicochemical properties, strong hydrogen bonding networks, and improved mechanical robustness.
In this work, a hydrogel composed of poly (vinyl alcohol) (PVA) and poly (NaSS-co-SBMA) was synthesized and used as a polymer matrix to produce eutectic gels through a solvent exchange process. Several DES systems were prepared by combining hydrogen bond donors (glycerol, ethylene glycol, and urea) with hydrogen bond acceptors (choline chloride, zinc chloride, calcium chloride and lithium chloride). These DES formulations were introduced into the hydrogel network to investigate how solvent composition influences the structure and properties of the resulting gels.
The DES-infused hydrogels were characterized using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) to evaluate thermal stability and phase behavior. Dynamic mechanical analysis (DMA) and Instron tensile testing were used to determine viscoelastic and mechanical properties. Ionic conductivity was assessed using an LED-based conductivity test to evaluate ion transport through the gel network.
This work provides insight into how different DES compositions influence the thermal, mechanical, and conductive behavior of eutectic gels and highlights their potential as conductive soft materials for flexible electronic and energy storage applications.

70 Sk Md Imdadul Islam

PhD student in Civil Engineering

Collaborator(s): Anil Kumar Baditha

Advisor(s): Yusuf Mehta

Henry M Rowan College of Engineering

Title:  Evaluating Storage Stability and Performance Characteristics of Recycled Composite Plastic Modified Asphalt Binders

Abstract: The study evaluated the storage stability and performance characteristics of composite plastic modified binders at dosages of 1% to 3%, using polyethylene grafted maleic anhydride (PE-g-MA) and Reactive Elastomeric Terpolymer (RET) stabilizers. Thermal characterization of plastics was carried out using differential scanning calorimetry, showing melting points between 130–155°C, suitable for wet blending applications. Storage stability was evaluated using complex shear modulus separation index and fluorescence microscopy. The results revealed improvement in storage stability with both stabilizers up to 2% dosage, with RET achieving more uniform and homogeneous plastic dispersion in asphalt binder matrix. Furthermore, rheological properties were evaluated through Superpave performance grading (PG), Multiple Stress Creep Recovery (MSCR), and Linear Amplitude Sweep (LAS). RET increased the high PG of plastic modified binders by up to three grades for HDPE+PP (HP) and two grades for LDPE+HDPE+PP (LHP), while PE-g-MA had a minimal impact on high PG. Both stabilizers maintained the low PG of the base binder, with RET providing greater improvement than PE-g-MA. The rutting performance increased significantly with RET, by enhancing recovery and lowering Jnr, outperforming PE-g-MA. With the addition of plastic, the fatigue performance was degraded. However, the use of stabilizers mitigated this effect, with RET and PE-g-MA enhanced fatigue lives by approximately 1500% and 270%, respectively. Overall, RET stabilizer was more effective than PE-g-MA in improving storage stability and performance characteristics. These findings suggest that composite plastic modified binders with up to 2% dosage combined with stabilizers can offer better storage stability and performance for sustainable pavement applications.

71 Anil Kumar  Baditha

Post-Doctoral Fellow in Civil Engineering

Collaborator(s): Sk Md Imdadul 

Advisor(s): Yusuf Mehta

Henry M Rowan College of Engineering

Title:  Evaluating the Rheological and Aging Characteristics of Plastic Modified Asphalt Binders Incorporating Reactive Elastomeric Terpolymer

Abstract: This study investigates the rheological behavior and aging characteristics of plastic modified asphalt binders incorporating two types of Reactive Elastomeric Terpolymer (RET) stabilizers with different melt flow indices (MFI). A comprehensive experimental program was conducted to assess the impact of RETs on the performance of plastic modified binders. Separation index and fluorescence microscopy was used to evaluate storage stability, while rheological behavior was analyzed through Black Space diagrams and performance grading. Aging effects were examined using low temperature grade, ΔTc, and LAS testing. Additionally, rutting resistance was assessed under elevated stress levels using MSCR test. Results showed that RET stabilizers improved the storage stability of plastic modified binders, with the higher MFI stabilizer being more effective. Black Space analysis revealed the formation of elastomeric networks and morphological complexity due to RETs, with RET B (lower MFI) exhibiting higher stiffness and lower phase angles, indicating improved rutting resistance. RETs also enhanced high temperature PG by four grades and restored low-temperature PG, mitigating the reduction caused by plastics. Although ΔTc values decreased with plastic addition, indicating increased cracking risk, stabilizers markedly improved ΔTc, enhancing resistance to low-temperature cracking. Moreover, RET stabilizers enhanced fatigue life by 660-1200% compared to plastic-modified binders. Under extended aging, RET-modified binders showed slightly greater ΔTc and fatigue life reductions but still outperformed other binders. The combination of 2% plastic and RET B provided superior rutting resistance, reducing Jnr by 70% and increasing recovery by 3,000%. The results emphasize the potential of RETs in developing high performance binders.

72 Anil Kumar Baditha

Post-Doctoral Fellow in Civil Engineering

Collaborator(s): Sk Md Imdadul

Advisor(s): Yusuf Mehta

Henry M Rowan College of Engineering

Title:  Performance Evaluation of Microencapsulated Phase Change Material Modified Asphalt Mixtures Using Steady and Non-Steady Temperature Tests

Abstract: This study evaluates the performance and thermoregulation behavior of asphalt mixtures modified with Microencapsulated Phase Change Materials (MPCMs) under both steady and non-steady-state test conditions. Three MPCMs with melting points of 6°C (M6), 28°C (M28), and a combination of both (M6+M28) were incorporated into asphalt mixtures using PG 58-28 binder through proportional method. Different laboratory tests were conducted to assess their effects on performance, including parameters such as dynamic modulus for stiffness/viscoelastic property, RTindex for rutting resistance, indirect tensile strength for low-temperature cracking, tensile strength ratio (TSR) for moisture-damage resistance, temperature difference, and CTindex for cracking resistance under non-steady-state conditions. The dynamic modulus assessment showed that all modified mixtures had lower stiffness than the control mixture, suggesting binder softening due to partial MPCM breakage. Under steady-state conditions, RTindex was slightly reduced for M28-containing mixtures, while low-temperature cracking resistance improved for all MPCM-modified mixtures. Further TSR ratio testing showed that all MPCMs mixes showed lower moisture resistance whereas M6 showed higher resistance to moisture-induced damage compared to other mixes. Temperature regulation tests confirmed that surviving MPCM particles increased temperatures of asphalt mixes, with the M6+M28 mix showing the most notable effect. Lastly, under non-steady state conditions, CTindex improved significantly at low temperatures for M6 and M6+M28 mixtures. Overall, the study highlights the potential of M6 and M6+M28 mixtures to maintain better encapsulation integrity, enhance cracking resistance and temperature control without significantly compromising other performance aspects.

73 Mark  Boata

PhD student in Chemical Engineering 

Advisor(s): Dr. Kenneth Lau

Henry M Rowan College of Engineering

Title: Understanding the Impact of Oxidative Chemical Vapor Deposited Electrically Conducting Polymer Coatings on Hard Carbon Anodes for Sodium-Ion Batteries

Abstract: The growing electrification of transportation and infrastructure requires electrochemical energy storage systems that are low-cost, scalable and based on abundant resources. Sodium-ion batteries (NIBs) are promising alternatives to lithium-ion batteries for grid-scale and large-format applications because sodium is abundant and inexpensive. However, their broader adoption is limited by challenges associated with hard carbon (HC) anodes. Although HC is attractive due to its low cost and availability, its unstable anode electrolyte interphase causes extensive first-cycle electrolyte decomposition and sodium loss. This leads to low initial coulombic efficiency (ICE), rapid impedance growth, poor rate capability, and reduced cycle life. This work investigates oxidative chemical vapor deposition (oCVD) of poly(3,4-ethylenedioxythiophene) (PEDOT) as a vapor-phase coating strategy to stabilize HC anodes with conformal and conductive thin films. Unlike solution-based coatings, which often show poor conformity on porous, high surface area carbons, oCVD is a one step, solvent free process that enables direct formation of thin, conformal PEDOT films from vapor phase reactants. PEDOT is intrinsically conductive and mechanically stable, making it a promising artificial interphase for HC. The goal of this research is to understand how oCVD PEDOT process conditions and coating chemistry influence HC properties and NIB electrochemical behavior. Specifically, this work will determine how oCVD process conditions control coating conformality, conductivity and porosity; evaluate how PEDOT film chemistry influences ICE, rate capability and cycling stability and we extend this approach to biomass-derived HC from spent coffee grounds to assess whether PEDOT coatings can reduce biomass variability and improve performance consistency, properties electrical performance. The ICE increased from 71% to 93% for bare and coated synthetic HC. 62 to 90% for bare and coated biomass HC. Overall, this research seeks to advance hard carbon anodes toward high-efficiency, long-life sodium-ion batteries and establish broader interfacial engineering strategies for porous energy-storage materials.

74 Terry Coley

PhD student in Chemical Engineering 

Advisor(s): Xiaohui Xu

Henry M Rowan College of Engineering

Title:  Phase-Separated Thermoresponsive Janus Hydrogel Beads

Abstract: Janus hydrogels are rapidly becoming a powerful class of asymmetric soft materials due to their ability to spatially separate chemical composition and functionality within a single material. This work presents a phase separation strategy for fabricating Janus hydrogel beads composed of a thermo-responsive polymer and a hydrophilic domain. This work uses the temperature-dependent hydrophobic transition of the thermo-responsive polymer to induce a controlled polymer phase separation during the bead formation. A thermo-responsive polymer solution is mixed with a hydrophilic monomer solution and then introduced into a heated oil bath maintained above the lower critical solution temperature (LCST) of the thermo-responsive polymer where it becomes increasingly hydrophobic and collapses. This thermally triggered change drives phase separation between the thermo-responsive polymer solution and the hydrophilic monomer solution. The polymerization of the hydrophilic monomer solution within this environment stabilizes the separated domains which results in the formation of Janus hydrogel beads with two chemically distinct compartments. One hemisphere consists of the thermo-responsive polymer network, while the opposing hemisphere forms a hydrophilic polymer network. This phase separation method enables the formation of Janus hydrogel beads without the need for complex microfluidic devices or multi-step processes. This work has potential applications in soft robotics, stimuli-responsive systems, or directional actuation. Overall, this work successfully demonstrates a simple and scalable strategy for producing structurally anisotropic hydrogel materials through thermally induced phase separation.

  

75 Ethan Cantor

PhD student in ECE Program 

Advisor(s): Dr. Jie Li

Henry M Rowan College of Engineering

Title:  Optimal Community Resilience of Interdependent Cyber-Physical-Social Power Systems

Abstract: As natural and human-caused threats to electric power systems increase in frequency and severity, communities face a growing risk of prolonged power outages. While prior research has developed optimization-based recovery strategies to reduce outage duration, most approaches focus exclusively on technical performance metrics and overlook the societal and emotional consequences of restoration decisions. The limited body of work that incorporates community well-being often neglects the growing interdependence between electric power systems and cyber-communication networks, which are increasingly essential for modern grid operation and control. This research addresses these gaps by developing an optimization-based community recovery framework that integrates cyber-physical interdependence with dynamic emotional states. The proposed model requires both the electric power system and the supporting communication network to be operational for restoration actions to occur. Community well-being is represented through evolving emotional states, such as fear and cooperation, which respond to outage duration, restoration progress, and emergency management interventions.

The framework also models advanced recovery strategies implemented by local Offices of Emergency Management, including demand response programs and on-site resource sharing. While these actions may accelerate physical system recovery, they can impose social burdens that negatively influence community emotional states, creating realistic tradeoffs between operational efficiency and human well-being. Results are expected to demonstrate that explicitly modeling emotional dynamics alters optimal restoration decisions and leads to improved overall community resilience. This work advances power system restoration planning toward a more human-centered, cyber-physically integrated paradigm for managing energy emergencies.

76 Lilliana De Salas

PhD student in Complex Biological Systems 

Advisor(s): Nick Whiting

College of Science and Math (CSM)

Title: The effects of CQD application on Glycine max

Abstract: Recent decades have seen agricultural advancements with the aim of increasing crop production through sustainable practices to mitigate the harmful environmental effects of synthetic fertilizers and pesticides. Nanotechnology has been employed in an array of agricultural applications such as nanofertilizers, nanopesticides, and in increasing crop resilience to a variety of stressors. However, many nanoparticles have been found release harmful compounds, dependent upon particle composition, into soils and waterways as they degrade. Carbon-based quantum dots (CQDs) are valued for their favorable optical and electronic properties while being inexpensive and biologically friendly. Here, we examined the effects that nanofertilization of CQDs, synthesized from citric acid and urea, have on the growth of soybeans (Glycine max) at varying concentrations. The goal of this experiment was to determine the optimal concentration of CQDs needed to increase soybean growth, without causing damage to the plant.

77 Cheyenne  Woodward

PhD student in Geology 

Advisor(s): Lily Pfeifer

College of Science and Math (CSM)

Title:  Geochemistry and Petrography of Upper Devonian (Famennian) Paleosols in the Appalachian Basin of Central Pennsylvania, USA

Abstract: To test the hypothesis of alpine glaciation in the late Devonian (Famennian) at subtropic latitudes, we present new major element geochemistry and petrographic thin section images from upper Devonian paleosols collected from north-central Pennsylvania (Appalachian Basin).  Paleosols, ancient soils, record climate conditions at the time of formation. Major element geochemistry is used to calculate the intensity of weathering which is used for paleoclimate interpretations, and microfabrics observed in paleosol thin section petrography yield important clues about the soil type and degree of pedogenesis in the system. Twenty three samples were collected from the Catskill and Lockhaven Formation paleosols for geochemical analysis, and seven of these were imaged in thin section. Catskill and Lockhaven Formation strata include terrestrial to transitional/shallow marine facies of the Acadian Wedge. Previous work on Catskill Formation paleosols document an apparent increase in arid climate conditions, despite overall paleoclimatic interpretations from the Famennian which document an upsection transition from arid to increasingly humid climate conditions during this time. Preliminary results of paleoweathering indices as calculated from new major element geochemistry inform that there was intermediate chemical weathering in the system during the Famennian. Further work is needed to assess whether the geochemistry of Upper Devonian paleosols in the Appalachian Basin are consistent with upsection increase or decrease in humidity, or if they are consistent with glaciogenic conditions in the paleohighlands (Appalachian Mountains).

78 Kelsey Barker

PhD student 

Advisor(s): Aaron Barth

School of Earth and Environment

Title:  Polythermal conditions for the southeastern Laurentide Ice Sheet as determined through 36Cl exposure ages and ice sheet modeling in the Adirondack Mountains of NY, USA

Abstract: During the last glacial period, the Laurentide Ice Sheet (LIS) contained 75-85 m of global sea-level equivalent making it the largest ice sheet by volume and a substantial influence within the climate system. Constraining the timing and rate of LIS deglaciation is critical to evaluate its contribution to sea-level rise, abrupt climate changes, and associated feedbacks. Studies of LIS retreat through the northeast United States have used various geochronometers (e.g., surface exposure dating, radiocarbon, varves) and methods to constrain changes in the vertical and lateral components of the ice sheet. Yet, reconstructions of the LIS in the Northeastern United States exhibit areas of limited geochronologic data leading to assumptions about the character and timing of ice-sheet retreat. Here we present new 36Cl surface exposure ages from the High Peaks Wilderness of the Adirondack Mountains paired with transient ice-sheet model simulations of LIS retreat through the region. These results indicate a rapid deglacial sequence of the LIS, fully exposing the High Peak Wilderness within a 500-year interval at ~13.5 ka + 0.3 (1sigma). This timing is consistent with modeled deglaciation rates across the Adirondacks and suggests a more rapid retreat than previously identified in the region. The timing of local deglaciation is younger than previously proposed, while remaining in agreement with minimum-limiting radiocarbon ages indicating reoccupation of fauna and flora by ~13 cal ka. Model simulations demonstrate reduced ice-flow velocities in the mountains relative to adjacent lowlands, suggesting that subglacial topography may have impeded ice retreat in the Adirondack High Peaks. Together, these data shift the timing of deglaciation in this region to the Allerød warm interval and highlight the sensitivity of the LIS to abrupt climate warming. These findings also underscore the importance of topographic controls on ice-sheet dynamics and retreat patterns during deglaciation.

79 Moayad Al Issa

PhD student in Civil Engineering 

Advisor(s): Yusuf Mehta

Henry M Rowan College of Engineering

Title:  Field Evaluation of Low-Temperature Cracking Performance of Highly Elastic Asphalt Mixtures Under Extreme Cold Conditions in Alaska

Abstract: Extreme thermal expansion-contraction cycles in cold regions can cause significant thermal stresses in asphalt mixtures and result in thermal cracking. The current study focuses on evaluating the in-situ thermal cracking resistance of pavement test sections made using Highly Elastic Asphalt Binders (HEBs) under extreme cold conditions. Four pavement test sections prepared with one control binder (PG 52-28) and three HEBs were constructed in Fairbanks, Alaska. The HEB mixtures were prepared using binders modified with 7.5% Styrene Butadiene Styrene (SBS) polymer and two softening agents namely hydrolene oil (7%) corn oil (7%, and 14%), respectively. Asphalt Strain Gauges (ASGs) and thermocouples were installed in the hot mix asphalt (HMA) layers to monitor field-induced thermal strains from August 2024 to July 2025. Daily and seasonal temperatures and respective HMA strain variations were recorded to understand the criticality of strain amplitudes under field conditions. Results showed that HMA mix temperatures were warmer throughout the monitoring period and exhibited a nonlinear relationship with ambient air temperatures. HMA mixtures exhibited positive tensile strains and negative compressive strains during the day-night thermal expansion-contraction cycles. HEBs showed higher strain recovery capacity during both summer and winter seasons due to their enhanced flexibility by softening agents. Interestingly, HEB mixtures showed higher and lower cyclic strain variations during winter and summer seasons, respectively. HEB mixtures exhibited higher CTE and CTC values during winter, whereas lower CTE and CTC values during the summer season, respectively.

80 Vid Liam Stijelja

PhD student in Mechanical Engineering 

Advisor(s): Dr. Aditya Lele

Henry M Rowan College of Engineering

Title:  Molecular Dynamics Study of Thermal Conductivity in Silicon with a Combination of Point Defects

Abstract: Efficient heat dissipation is a critical bottleneck in modern microelectronics. This study utilizes Green-Kubo equilibrium molecular dynamics (EMD) simulations to quantify how combination of point defects (vacancies and Ge substitutions) impact thermal conductivity in silicon. Simulations at 300 K revealed that combined defect concentrations of 0.05% and 0.25% drastically reduce thermal conductivity by 39% and 81%, respectively, compared to pure silicon.
To determine if these phonon scattering mechanisms act independently, the molecular dynamics results were evaluated against a derived Matthiessen’s rule prediction.
Deviations between additive predictions and full simulation data remained within uncertainty limits, confirming that Matthiessen’s rule holds at these concentrations. This establishes a verified baseline for independent scattering, providing a foundation to explore the physical "breaking point" where defect overlapping may cause classical approximations to fail. Future research directions include investigating the rule’s validity across diverse defect combinations or examining how temperature fluctuations influence the additivity of anharmonic phonon-phonon scattering. Additionally, spectral analysis via Phonon Density of States (PDOS) will be utilized to pinpoint vibrational modes potentially susceptible to defect coupling.

81 Temitope Okunbamu

PhD student in Materials Science & Engineering 

Advisor(s): DR Dong Mei

College of Science and Math (CSM)

Title: Development of a Microcontroller-Driven Electrochemical Sensing Platform for PFAS Detection Using an Extended-Gate Field-Effect Transistor

Abstract: Per- and polyfluoroalkyl substances (PFAS), commonly known as "forever chemicals," pose a critical threat to public health due to their persistence, bioaccumulation, and toxicity at ultra-trace concentrations. Current gold-standard detection methods such as LC-MS/MS, while highly sensitive, are expensive, laboratory-dependent, and unsuitable for real-time field deployment. This work presents the development of a portable, microcontroller-based extended-gate field-effect transistor (EGFET) sensing platform aimed at enabling on-site, real-time PFAS monitoring in water. The system integrates an ESP32 microcontroller with an EmStat Pico bipotentiostat to perform electrochemical measurements — including chronoamperometry (CA) and linear sweep voltammetry (LSV) — without reliance on dedicated laboratory software. A p-type MOSFET in an EGFET configuration with a bare indium tin oxide (ITO) electrode and Ag/AgCl reference electrode serves as the sensing interface.  Ongoing work focuses on completing the standalone ESP32 firmware for autonomous data acquisition, followed by functionalization of the ITO electrode with a fluorophilic Krytox PEG diamide (KPD) sensing layer to enable selective PFAS detection. This portable platform offers a low-cost, field-deployable solution that bridges the gap between regulatory ultra-trace detection demands and practical environmental water quality monitoring.

82 Samuel Addai

Master's student in Civil Engineering 

Advisor(s): Yusuf Mehta

Henry M Rowan College of Engineering

Title: Evaluation of Healing Potential of Asphalt Mixtures Modified with Nanoparticle-Enhanced Binders

Abstract: This study investigates the fatigue and healing performance of asphalt mixtures incorporating nanoparticle-modified binders, using two nanomaterials—nanoclay and nanosilica. Two performance-graded binders (PG 58-28 and PG 76-22) were selected to assess the effect of binder type on healing behavior. Asphalt mixtures were prepared using granite aggregates and UFGS Gradation 3, then subjected to mechanical and simulation-based evaluations. Cyclic fatigue testing was conducted using the Asphalt Mixture Performance Tester (AMPT) at 25°C, with a strain amplitude of 800 microstrains and a loading frequency of 10 Hz. To simulate in-service conditions, 10- and 20-minute rest periods were introduced after 25% of the specimen's estimated fatigue life, representing early-stage fatigue damage accumulation. Healing was quantified by comparing the number of cycles to failure (Nf) before and after rest period. Dynamic modulus testing and FlexPAVE™ simulations were also performed to assess viscoelastic behavior and long-term pavement performance. Results showed that both nanoclay and nanosilica modified mixtures exhibited notable improvements in fatigue life relative to the control, with nanoclay modified mixtures achieving the highest fatigue life improvement, while nanosilica modified mixtures demonstrated consistent intermediate gains across both binder types. FlexPAVE™ simulations indicated a 37% reduction in total fatigue damage over 20 years for nanoclay-modified mixtures with rest periods. Rutting and cracking resistance also improved significantly, as observed from APA and IDEAL-CT tests. The findings confirm that nanomodification, especially with nanoclay, enhances the intrinsic healing capacity, fatigue resistance, and durability of asphalt mixtures. Incorporating rest periods in design further optimizes long-term performance, offering a sustainable strategy for modern pavement systems.

83 Bably Das

PhD student in Mechanical Engineering

Collaborator(s): T. Timmons, Elias; Koohbor, Behrad; Haas, Francis M. 

Advisor(s): Behrad Koohbor, Francis M. Haas

Henry M Rowan College of Engineering

Title: Developing a Numerical-Experimental Method to Identify Material Constitutive Parameters for High Strain Rate Conditions

Abstract: In the cold spray additive manufacturing process, microparticles impact a substrate at very high velocities. Impacts potentially form mechanical bonds between particles and substrate, and the strength of these bonds depends primarily on the extent of plastic deformation within the particle. However, accurate prediction of particle deformation and interfacial dynamics remains challenging due to uncertainty in the constitutive parameters governing strain rate-dependent plasticity during impact events. This study advances a framework for identifying the Johnson-Cook (JC) constitutive model parameters for Al2017-T4 particles impacting a hardened steel substrate under controlled single-particle impact conditions. A two-dimensional axisymmetric finite element simulation is established in ANSYS Explicit Dynamics, incorporating JC plasticity parameters across a range of impact velocities. Simulated impact-rebound events obtained from a range of model parameters are compared with experimental measurements. Simulations show that variations in the JC strain rate coefficient C and thermal softening exponent m significantly influence the rebound velocity and the peak equivalent plastic strain fields within the particle. A systematic variation of these material parameters is used to quantify and minimize the differences between the numerical predictions and the experimental measurements, thereby enabling identification of the JC model parameter set that most realistically represents the material behavior under the extreme conditions considered. The proposed numerical framework provides a practical approach for determining constitutive model parameters for materials subjected to high strain rates. The results offer useful insight for developing constitutive models and improving numerical simulations of high strain rate processes, potentially reducing the need for complex and costly experiments.

84 Tyler Paupst

PhD student in Mechanical Engineering 

Advisor(s): Paromita Nath

Henry M Rowan College of Engineering

Title: Identification of Constitutive Model Parameters Using Bayesian Calibration

Abstract: This work presents a framework for integrating physics-based simulations with experimental observations through a Bayesian formulation, reducing the number of required experimental trials and quantifying the uncertainty for calibration of constitutive model parameters. To demonstrate the approach, the Johnson-Cook (JC) plasticity model is assumed to accurately represent the investigated material as the constitutive model for high strain rate impact events. Synthetic observations are generated for stress-strain curves to represent experimental observations, which are used to evaluate calibration performance.

Two calibration strategies are examined: Simultaneous Bayesian Calibration (SimBC) and Sequential Bayesian Calibration (SeqBC). At elevated strain rates and temperatures, SimBC converges to posteriors containing the true parameter values; however, coupling between thermal and rate effects limits parameter identifiability. SeqBC identifies most parameters correctly but introduces bias in the thermal sensitivity term due to parameter interaction. The demonstrated framework improves accuracy of calibrated parameters in conditions with poor identifiability.

85 Noor Aldin Alzghoul

PhD student in Mechanical Engineering 

Advisor(s): Aditya Lele

Henry M Rowan College of Engineering

Title: ML Accelerated Computational Prediction Of  Catalytic Nanoparticle Shapes

Abstract: Accurate accounting of the different facets is critical for understanding the catalytic activity of metal nanoparticles in heterogeneous catalysis. The morphology of nanoparticles is governed either by their growth kinetics or by the operating conditions. Temperature is one of the most important parameters controlling both the shape and the facet distribution of catalytic nanoparticles. Modeling the shape and facet distribution of nanoparticles at finite temperatures is computationally challenging and expensive, even under non-reactive conditions. In this work, we explore a multi-fidelity training strategy for efficiently developing machine-learning interatomic potentials (MLIPs) capable of accurately modeling the shape evolution of platinum (Pt) nanoparticles and the distribution of their facets and vertices over time. Our approach combines data from foundational machine-learning potentials with high-accuracy density functional theory (DFT) calculations to train an efficient MLIP. The resulting MLIP is significantly faster to train than models relying solely on high-accuracy data and substantially faster to evaluate than the foundational ML models.

86 Ronald Borja-Roman

PhD student in Chemical Engineering 

Advisor(s): Dr. Kirti M. Yenkie

Henry M Rowan College of Engineering

Title: Autonomous Characterization of High-Strain-Rate Deformation Using Vision Foundation Models

Abstract: Understanding how materials deform under extreme conditions is essential for the design of advanced structures in aerospace, energy, and manufacturing. However, high-strain-rate phenomena occur over very short temporal and spatial scales, making their quantitative characterization experimentally demanding and often reliant on manual analysis. As a result, many existing material databases capture only limited aspects of the transient deformation process, constraining the development and validation of predictive constitutive models.

Here we introduce an autonomous vision-based framework that converts high-speed impact videography directly into quantitative deformation measurements. The approach leverages vision foundation models, including Grounding DINO and the Segment Anything Model, to detect and segment impacting particles across high-speed video sequences without task-specific training. From the resulting spatiotemporal masks, the system automatically derives deformation metrics including velocity evolution, flattening ratio, and deformation rate, while performing frame-level uncertainty estimation to assess measurement reliability. This enables automated identification of low-confidence predictions and improves the reproducibility of extracted experimental data.

We validate the framework on high-speed impact experiments of aluminum alloys and demonstrate that the extracted deformation measurements enable calibration of the Johnson–Cook constitutive model, reducing characterization time and experimental cost. By transforming raw high-speed video into structured physical measurements, this work establishes a scalable methodology for automated characterization of material behavior under extreme conditions. More broadly, the proposed framework illustrates how vision foundation models can support autonomous experimental workflows and the development of next-generation materials databases.

87 Amit Singh

PhD student in Mechanical Engineering 

Advisor(s): Wei Xue

Henry M Rowan College of Engineering

Title:  STRUCTURE-PROPERTY RELATIONSHIPS IN POLYMER DIELECTRICS FOR HTS CABLE INSULATION APPLICATIONS

Abstract: High-temperature superconducting (HTS) power transmission cables offer significant advantages for modern electrical grids, including high current density, reduced resistive losses, and compact system design. However, the long-term reliability and efficiency of HTS cables strongly depend on the performance of dielectric insulation materials operating under elevated temperatures, electrical stress, and mechanical loading. Conventional polymeric insulation materials often exhibit limited thermal stability and dielectric strength under such demanding conditions.
This research focuses on the development and characterization of advanced dielectric polymer nanocomposites designed to enhance insulation performance for HTS power transmission applications. Polymer matrices (PAA/PI) are reinforced with nanoscale ceramic fillers (SiO2, Al2O3) to improve dielectric strength, thermal stability, and mechanical integrity while maintaining processability. Controlled dispersion and interfacial interactions between the polymer matrix and nanofillers play a critical role in achieving improved electrical insulation performance.
A combination of material synthesis and comprehensive characterization techniques is used to evaluate the structure–property relationships of the developed nanocomposites. Microstructural analysis is performed using scanning electron microscopy (SEM), while chemical bonding and functional group interactions are examined through Fourier-transform infrared spectroscopy (FTIR). Mechanical properties are evaluated through tensile testing and thermal stability are assessed through TGA and DSC.
The results demonstrate that the incorporation of optimized nanofiller concentrations significantly enhances dielectric performance, thermal stability, and mechanical strength compared with conventional polymer insulation materials. These improvements highlight the potential of dielectric polymer nanocomposites as next-generation insulation materials for reliable and energy-efficient HTS power transmission systems. The findings contribute to advancing materials design strategies for high-performance electrical insulation in next-generation energy infrastructure.

88 Prithvi Ravi

PhD student in Mechanical Engineering 

Advisor(s): Amin Nozariasbmarz

Henry M Rowan College of Engineering

Title:  Achieving Compositional Uniformity in (BixSb1-x)2Te3 Thermoelectric Alloys via Microwave Processing

Abstract: Bismuth antimony telluride, (BixSb1-x)2Te3, is one of the most important thermoelectric materials for thermal energy harvesting and cooling applications. Several techniques have been developed to fabricate high-performance (BixSb1-x)2Te3 alloys, including melting techniques, spark plasma sintering, and microwave processing. Processing conditions must carefully control non-equilibrium phases, crystallinity, grain size, structural uniformity, and texturing. Among these approaches, microwave processing is one of the newest and most promising techniques, offering capabilities that are not easily achievable using conventional methods. Conventional processing routes rely on surface-to-core heating, which introduces temperature gradients that lead to non-uniform heating and diffusion behavior. This can result in degradation or spatial variation in transport properties. Therefore, maintaining uniform elemental distribution is essential for achieving consistent transport properties.
  Microwave processing provides an alternative approach through volumetric heating, enabling rapid and distributed heating throughout the bulk of the material. In this study, (BixSb1-x)2Te3 alloys were fabricated using microwave processing. Their compositional variation was analyzed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) mapping. Elemental maps of Bi, Sb, and Te across multiple regions of the sample demonstrated a uniform distribution with minimal compositional variation. Quantitative analysis further confirmed consistent elemental ratios throughout the bulk. These results suggest that microwave-assisted processing promotes homogeneous reaction kinetics within the material, enabling compositional uniformity in thermoelectric alloys and offering a promising pathway toward scalable and controlled synthesis of next-generation thermoelectrics.

89 Ian Renaud

Master's student in Materials Science & Engineering 

Advisor(s): Dongmei Dong

College of Science and Math (CSM)

Title:  Interfacial Engineering Strategies for Enhanced Electrochemical Detection of PFAS

Abstract: Poly- and Perfluoroalkyl substances (PFAS) are persistent environmental contaminants that pose health risks due to their bioaccumulation and chemical stability. Rapid, real-time response tests are a major challenge, particularly for portable and low-cost sensing mediums. In this work, my group investigates chemical sensing strategies utilizing interfacial engineering to selectively adsorb PFAS at a conductive sensor surface.

This work emphasizes the importance of rational interfacial design in electrochemical PFAS sensing and provides insight into how engineered sensor architectures can improve detection performance. The findings contribute toward the development of scalable sensing platforms for environmental monitoring and potential applications in complex aqueous matrices.

90 Cody Soper

PhD student in Materials Science & Engineering 

Advisor(s): Nicholas Whiting

College of Science and Math (CSM)

Title: Development of a Custom NMR Probe for Future 13C Detection of Carbon Nanoparticles

Abstract: Carbon-based nanoparticles are actively being investigated as next-generation magnetic resonance imaging (MRI) contrast agents due to their tunable surface chemistry, biocompatibility, and potential for hyperpolarization. In order to study nuclear spin dynamics and polarization behavior in these materials, a custom nuclear magnetic resonance (NMR) probe is being developed for operation within a Physical Property Measurement System (PPMS). The system will ultimately enable low-temperature studies of 13C nuclei in carbon nanoparticles, including investigations of brute-force hyperpolarization and spin-lattice relaxation (T1) behavior.

This work presents the design and initial testing of a prototype NMR probe incorporating a 3D-printed sample coil and a custom-built RF tune-and-match circuit. The probe was designed to interface with existing spectrometer hardware while fitting within the geometric constraints of the PPMS environment. Initial validation experiments focused on room-temperature proton detection to verify RF matching, coil performance, and signal acquisition capability.

A detectable proton free induction decay (FID) signal has been successfully obtained, demonstrating basic functionality of the probe and detection electronics. Ongoing work focuses on improving coil geometry, optimizing acquisition parameters, and increasing signal-to-noise ratio. These developments represent an initial step toward a fully integrated low-temperature NMR platform for studying the spin dynamics of carbon nanoparticles relevant to MRI contrast and hyperpolarization research.

91 James Turbett

Master's student in Mathematics: Applied Mathematics Concentration

Advisor(s): Helga Huntley

College of Science and Math (CSM)

Title: Analytic Solutions and Stability Analysis for the Kinematic Property Equations

Abstract: We present analytic solutions and a stability analysis for a system of nonlinear coupled differential equations that describe the time evolution of the four "kinematic properties", or oceanic velocity gradients, observed locally as one follows a single fluid particle flowing with the currents (the Lagrangian perspective).

Kinematic properties are relationships between oceanic velocity gradients that essentially quantify the notions of 2-dimensional fluid parcel deformation, rotation, and expansion/contraction.

For the general forcing case, we were able to analytically solve for the partially-steady solutions, where one dependent variable is constant and the others vary. For the unforced and divergence-forced case, we were able to analytically solve for the general solution.

92 Joseph Deckhut

PhD student 

Advisor(s): Zachary Boles

School of Earth and Environment

Title: Bendy or rigid? Interpreting fossil lifestyle based on bone bendability

Abstract: Lifestyles of fossil vertebrates are the interpreted living conditions, behaviors, and presumed daily life habits of the animals, as determined partly by morphological clues. These interpretations can be difficult for extinct organisms, though, due to the fragmentary nature of the fossil record. The polar section modulus method correlates the bendability or rigidity of the humeri and femora of extant organisms to their lifestyle. This method is shown to be reliable for avians, where two latest Cretaceous Antarctic fossil birds, Antarcticavis capelambensis and Vegavis iaai, were successfully assigned lifestyles based on 11 different morphospace plots from a dataset of 346 individual modern avians representing 65 different extant taxa. Antarcticavis plotted near shorebirds such as the Great Black-backed Gull, and Vegavis charted as an amphibious bird, similar to that of a Common Merganser. In this study, we plan to expand this work to modern and fossil turtles. Limb bones from modern turtles with known lifestyles (e.g., pelagic, shallow marine, freshwater, amphibious, terrestrial, and ambush predatory) will be measured and placed in morphospace plots to see if any correlation exists. If so, we will be able to incorporate fossil turtles into our dataset to interpret their lifestyles. In prior studies, turtle shell morphology and bone microstructure have been correlated with habitat preference. The polar section modulus method, if successful in turtles, may provide a complimentary or alternative method for interpreting lifestyles of fossil turtles.

93 Nicholas Pagliocca

PhD student in Mechanical Engineering 

Advisor(s): Mitja Trkov & Behrad Koohbor (Coadvised)

Henry M Rowan College of Engineering

Title:  A Formal Material Safety Specification for Soft Robotic Actuators Based on Strain Energy Functions and HOCBF-QP Based Control

Abstract: Soft robots are often claimed to be inherently safe, solely attributed to the fact that soft robots are less likely to damage their operating environment than rigid robots. This said, soft robots pose many physical hazards from material failure that are often overlooked in literature. To realize the next generation of soft robot controllers, it becomes imperative to develop formal notions of safety for soft robotic systems. Since soft robots achieve useful functionality through deformation, material failure is a universal safety concern for all soft robots. This work defines a formal notion of safety in the sense of material response, and a theorem for its enforcement using high-order control barrier functions (HOCBF) as constraints in quadratic program (QP) based controllers. Safe and unsafe sets are characterized based on constitutive model divergence from tensile test data. A theoretical model of an extensible soft actuator derived from first principles in hyperelasticity with inertial and viscous effects is used as a demonstrating example in simulation. Simulation results show that material safety can be enforced using our methodology and vastly outperforms simpler controllers using pressure clipping. This work lays the foundation for material aware nonlinear controllers for soft robotics and beyond.

94 Vaibhavsingh Varma

Mechanical Engineering 

Advisor(s): Mitja Trkov 

Henry M Rowan College of Engineering

Title:  Recovering Balance After Foot Slip with Robotic Exoskeleton Assistance: Modeling, Planning, and Device Development

Abstract: Preventing falls caused by gait perturbations such as slipping is critical for reducing fatal  injuries and healthcare costs, particularly among older adults. The location of recovery step, in  both sagittal and frontal planes of walking, plays a major role in the recovery process. To  address this, we propose a momentum-aware biped state estimation (MABSE) approach to  compute a safe recovery leg stepping location. In addition, we developed a pneumatically  actuated, cable-driven exoskeleton to assist users to achieve the desired recovery step location. A two-mass linear inverted pendulum was used to model stance phase gait independently in sagittal and frontal planes while considering foot slip. The resulting phase space of the dynamic  models in both planes is comprised of hyperbola manifolds. Recovery state to avoid fall was computed using MABSE by optimizing a cost function comprised of: 1) phase space-based pseudo-Riemannian distance derived from the hyperbolic manifold solution for the two-mass inverted pendulum dynamics, 2) instantaneous angular momentum difference between the current state and the estimated recovery state, and 3) phase space-based foot switch measure.The proposed approach serves as a high-level controller of exoskeleton device that assists subjects achieve predicted foot placement to recover balance. Further gait progression is driven toward a nominal stable walking manifold using a foot centre of pressure controller. The exoskeleton device developed to assist with slip recovery consists of pneumatically-actuated, cable-driven knee extension and hip abduction joints, along with a passive hip flexionextension joint. The joint movement for recovery is achieved by driving the active joints to make the human subject step down at the MABSE-based recovery step location. The results indicate that stepping at the MABSE predicted locations enables the instantaneous angular momentum to stabilize the body in a manner consistent with the patterns observed during the experiments among subjects who successfully recovered from slip. The exoskeleton device  aimed to assist with slip recovery can perturb the knee joint mid gait and forces a foot stepping down earlier, thus terminating the normal gait cycle. The device can also exert torque at the hip  to achieve hip abduction angle between 12-24 degrees and can increase subjects’ step width during walking to stabilize balance in frontal plane. We propose that the combination of  MABSE-based recovery stepping controller and assistance from the exoskeleton device would  be able to help human recover balance after a foot slip event. The future work includes human  subject experiments to validate slip recovery with exoskeleton assistance

95 Fazalay Beshal

Master's student in Civil Engineering 

Advisor(s): Mohammad Jalayer, PhD

Henry M Rowan College of Engineering

Title: Planning, and Device Development

Abstract: Transit Signal Priority (TSP) has evolved into a data-driven reliability strategy widely adopted to reduce bus delay and improve service performance in urban corridors. Although substantial research has examined algorithm design and localized performance impacts, there is a gap in linking technical architectures, operational logic, implementation barriers, performance matrices, and corridor-readiness conditions within a unified decision framework. This study addresses that gap by conducting a structured literature review, complemented by cross-case analysis of 11 U.S. deployments and structured stakeholder interviews (n = 9) to interpret contemporary practice. The review is organized around five research questions examining: (1) deployed TSP architectures and functional integration requirements; (2) prevailing trigger and grant logic patterns and their thresholds; (3) implementation and operational barriers across project phases; (4) defensible Key Performance Indicators (KPIs) and measurement pipelines; and (5) translation of evidence into a best-fit corridor selection framework. Across sources, three dominant architecture types were identified: controller-resident, centralized/server-based, and connected vehicle-enabled systems, each differentiated primarily by decision location and communications maturity. Deployments consistently converged on conditional priority with bounded actuation, typically limiting green extension to 6-10 s and red truncation to 6-8 s, while reported corridor travel time reductions commonly ranged from 5% to 20%. Barrier clustering revealed recurring integration, interoperability, and infrastructure-cost constraints, and KPI defensibility was strongly linked to data-pipeline readiness. The synthesis demonstrates that TSP effectiveness depends on alignment among architecture, operational thresholds, institutional capacity, and evaluation rigor. The study contributes an integration-oriented framework to guide scalable and context-sensitive TSP implementation.

96 Crystal Hutchinson

EdD student in Educational Leadership 

Advisor(s): Shelley Zion

College of Education

Title: Shifting Selves: The Multiracial Identity Assertion Framework

Abstract: Multiracial people live within a racial system that is structured by monoracial classification. Even as the multiracial population continues to grow, dominant racial structures remain organized around singular racial categories. This renders multiracial identity assertion as structurally constrained. Existing identity development models do not fully explain how multiracial individuals assert, negotiate, and reposition their identities across contexts shaped by racial hierarchy. The Multiracial Identity Assertion Framework addresses this gap by integrating the Model of Multiracial Racialization, the Lifespan Model of Ethnic-Racial Identity, and the Five Mixed-Race Identities within Critical Multiracial Theory framing. Rather than treating identity as a fixed typology, this framework conceptualizes identity assertion as dynamic, contextually negotiated, and structurally bounded by monoracial systems of power. Empirical research using the framework changes how interdisciplinary fields such as sociology, psychology, counseling, and education understand identity development and assertion across the lifespan.

97 Makaylah Michel

Master's Student in Criminal Justice 

Ric Edelman College of Communication, Humanities and Social Science

Title: The Intersectionof Race and the Discussion of Jury Nullification

Abstract: At every level of the criminal legal system, colorblind laws, policies, and decision-making are used by ordinary citizens to perpetuate racial disparities and maintain subordination from the Black population. Specifically, jury nullification, or the process of acquitting a guilty defendant, has been racialized to enact injustice on Black communities. Despite the normalcy of colorblind rhetoric in the criminal legal system, approaching jury nullification from a colorblind perspective fails to acknowledge the role of race in court case outcomes nor does it achieve justice for individuals being judged by a system that incorporates colorblind racism. This exploratory study aims to critically examine academic literature regarding jury nullification in the criminal justice and criminology discipline. Guided by Paul Butler’s essay, “Racially Based Jury Nullification: Black Power in the Criminal Justice System,” a systematic review of criminal justice and criminology literature will be conducted to analyze authors’ use of race-consciousness as a framework. An inductive approach will be used to inform a keyword frequency analysis, and statistical tests (chi square and logistic regression) will be run to analyze the discussion of jury nullification. Not only will this study advance Critical Race Theory by applying a derivative framework to the topic of jury nullification, but this study will inform scholars and policy makers about the role race plays in the perception, portrayal, and implementation of policies in the criminal legal system more broadly. 

98

Nicole Abbott

Master's student and research assistant 

Advisor(s): Mahbubur Meenar

School of Earth and Environment

Title: Access, Equity, and Urban Agriculture: Trends in Selected NJ Municipalities

Abstract: Urban Agriculture is the cultivation and distribution of agricultural products in an urban setting. New Jersey municipalities are utilizing urban agriculture as a holistic strategy to address food insecurity and equity to varying degrees of success. This study looks to identify factors that influence the success of urban agriculture as a food equity intervention in selected New Jersey municipalities. The Rutgers’ 2022 NJ Urban Agriculture Web Map was utilized as a basemap to identify urban agricultural instances (UAIs) active in 2022 in 15 select NJ municipalities. Municipalities were selected based on equal geographic representation and UAI count distribution. Satellite imagery of NJ during 2018, 2021, and 2024, provided by NASA, was utilized to confirm active cultivation of agricultural products via visual inspection. Trends regarding growth and shrinkage of instances’ agricultural production and overall UAI count were identified and analyzed utilizing visual and Python analysis. Furthermore, GIS analysis was utilized to identify socioeconomic and demographic trends within the broader city context and a defined buffer area of active UAIs. Through this, weighted area population analysis was conducted to identify outreach effectiveness. Further research into municipal codes of law and relevant food system plans may provide a broader context and reveal further trends/determinants of success for urban agricultural intervention strategies. This study highlights the use of geospatial technologies to analyze trends and patterns in urban agriculture practices in selected cities. The methodology should be applicable to other urban areas.

99 AHMED IMTIAZ ZAMEE

PhD student in Civil Engineering 

Advisor(s): Mohammad Jalayer

Henry M Rowan College of Engineering

Title: From Traditional Work Zones to Smart Work Zones: Improving Work Zone Safety

Abstract: Work zones are necessary for maintaining and upgrading transportation infrastructure, but they also introduce significant safety risks for both roadway users and construction workers. Traditional work zone management methods often rely on static traffic control devices and manual monitoring, which may be insufficient for addressing the dynamic conditions present in modern highway construction environments. In recent years, Smart Work Zone (SWZ) technologies have emerged as an effective approach to improve safety and operational efficiency through the use of real-time data, automated warning systems, and intelligent transportation technologies. This study examines the transition from traditional work zone practices to smart work zone systems and their role in improving overall work zone safety. The research reviews commonly implemented smart work zone strategies such as queue warning systems, dynamic message signs, speed feedback displays, and connected monitoring devices that provide real-time traffic information to drivers and transportation agencies. Data were collected through literature review, agency documentation, and interviews with transportation professionals involved in work zone planning and management. The findings indicate that smart work zone technologies enhance driver awareness, reduce the likelihood of rear-end collisions, and improve traffic flow through construction areas. Despite these benefits, challenges such as implementation costs, technology integration, and institutional coordination remain barriers to widespread adoption. The results of this study highlight the potential of smart work zone technologies to significantly enhance work zone safety and provide insights for transportation agencies seeking to modernize their work zone management practices.

100 Ladarion Hardison

Master's student 

Advisor(s): Mahbubur Meenar

School of Earth and Environment

Title: The Human Side of Air Quality: Lived Experience and Environmental Perception in Camden

Abstract: Environmental justice (EJ) communities, often low-income neighborhoods and communities of color, experience disproportionate exposure to environmental hazards due to historical patterns of industrial siting, transportation infrastructure, and land-use decisions. Air pollution is one of the most persistent environmental challenges affecting these communities. In many post-industrial cities, multiple pollution sources are concentrated in close proximity to residential areas, increasing cumulative exposure to harmful air contaminants and raising concerns about environmental health and quality of life.

This study focuses on Camden, a post-industrial city widely recognized as an EJ community. Camden faces several environmental challenges related to its industrial legacy and transportation networks. Sources of air pollution include nearby industries, major highways, and heavy truck traffic associated with industrial and port activities. Additional concerns include brownfields, scrapyards, and occasional tire burning. Residents also report persistent odor problems from certain facilities, including the county’s main wastewater treatment plant that serves the entire county.

This study examines community perceptions of air quality and air pollution using qualitative data collected through focus groups, interviews, and field observations. All materials were systematically analyzed using open, axial, and selective coding. We first describe the coding process used to organize the qualitative data. We then present key codes and themes that emerged from participant responses. Our conclusions are grounded in participants’ perspectives and highlight how residents perceive air pollution and what actions they believe are needed to improve air quality in their community.

101 Sean Olcese

Master's student 

Advisor(s): Qian He

School of Earth and Environment

Title: Sea-Level Rise and Tax-Base Vulnerability for New Jersey Coastal Municipalities

Abstract: Sea level rise and recurrent coastal flooding threaten coastal infrastructure and the fiscal stability of local governments that rely heavily on property taxes. Most local risk assessments focus on direct inundation and understate service isolation, when road disruption cuts off access to parcels outside flooded areas and can accelerate property devaluation. This study develops a parcel-level exposure framework for Southern New Jersey that quantifies both inundation and isolation under multiple sea level rise scenarios. Focusing on Salem, Cumberland, Cape May, and Atlantic Counties, we integrate parcel, road network, and land use information to evaluate how exposure changes when isolation is considered alongside flooding, and where risk concentrates across residential, commercial, and critical community-serving parcels. Results show that isolation affects substantially more parcels than inundation alone, indicating a broader tax base and service delivery risk than maps of flooded parcels suggest. Many parcels become cut off at relatively low sea level rise thresholds, approximately 2 to 4 feet, because access routes fail before widespread flooding occurs. Exposure in Southern New Jersey also emerges early and saturates quickly, driven largely by residential parcels. These findings help planners identify infrastructure weak points and fiscal vulnerability earlier in the sea level rise timeline and target adaptation actions before widespread inundation.

1. Stephanie Lezotte - CoEd

2. Yolanda Mack - HMRCoE

3. Frank Derby - HMRCoE

4. Renee Demarest - RV-TBES

5. Susanne Ferrin - HMRCoE

6. Tabbetha Dobbins - SGS

7. Others Added Later

• Evans Omondi, College of Science and Mathematics

• Aurelio Sanchez Ramirez, Rohrer College of Business

• Sharmin Sultana, College of Science and Mathematics

• Anand Kumar Upparapally, College of Science and Mathematics

• Nathaniel (Nate) Wypych, College of Science and Mathematics 

SCCA Scholar's Showcase Display

Students: Adam Bretz, Devonte Bishop, Eric Bradley, and Prithvi Ravi 

Advisor:  Dr. Amin Nozari

Title: Thermomagnetic Generators for Waste Heat Recovery

Description: Thermomagnetic devices can generate continuous electricity through cyclic heating and cooling, enabling the conversion of temperature gradients as low as 1 °C into electrical energy. This effect arises from the magnetic phase transition of ferromagnetic materials near their Curie temperature. In this Engineering Clinic project, students designed and fabricated a thermomagnetic generator that converts low-grade heat into electricity near room temperature. Gadolinium rings cycle between warm water and ambient air, causing changes in magnetization that drive continuous rotation and power generation. This work demonstrates the potential of thermomagnetic systems to harvest low-grade waste heat and convert it into useful electrical energy.

About the SCCA Scholar's Showcase: The Scholars' Showcase is an initiative from the Student Centers & Campus Activities (SCCA) designed to celebrate and promote research and scholarship across our campus community.  The Scholars' Showcase offers a welcoming and informal space where students, faculty, and staff can engage in meaningful conversations about academic inquiry, share their work, and connect with others from across disciplines. These programs will be held in the Student Center expansion, specifically in the demonstration area—a vibrant, high-traffic space ideal for discovery and interaction.