CV

EDUCATION AND TRAINING EXPERIENCE

Georgia State University

PhD in Computer Science, 2010

Master of Science in Computer Science, 2008

University of Illinois at Urbana-Champaign

Postdoc researcher in Department of Computer Science 2011-2013

PROFESSIONAL EXPERIENCES

University Honors Faculty w University of North Carolina at Charlotte w 2022 – 2027

Assistant Teaching Professor/Graduate Faculty w Department of Computer Science w College of Computing and Informatics w University of North Carolina at Charlotte w 2023 (August) – present

Lecturer/Graduate Faculty w Department of Computer Science w College of Computing and Informatics w University of North Carolina at Charlotte w 2018 – 2023 (May)

Tenure-track Assistant Professor w School of Engineering and Technology w Miami Dade College (MDC) w 2016 – 2018

Researcher w Center of Computational Science w University of Miami w 2014 – 2016

PROJECTS (The square brackets in each item enclose the associated papers)

CS EDUCATION PROJECTS [8 Publications]

·     Intelligent Tutoring System

a.  Educational data mining and predictive analytics. [Edu2, Edu7, Edu11]

b.  Methodologies of adaptive learning and teaching. [Edu1, Edu3]

c.  Auto-grading and auto-feedback of open-form responses [Edu9]

d.      Knowledge graph construction [Edu10]

·     Pedagogy Exploration

a.  Experiential learning. [Edu8]

b.  Teaching methodology in sequential or parallel programming courses. [Edu4]

·     Curriculum Development

a.  Data literacy (curriculum innovation of integrating fundamental data analytics skills into an information technology course through the project-based methodology with a goal of equipping college-wide students with data analytics skills). [Edu5, Edu6]

SCIENTIFIC PROJECTS [23 Publications]

·        Genomic big data analytics (mining big genomic data and identifying the sequence-specific transcription factor (TF)-TF interacting networks). [SC6, SC7]

·        Knowledge network data integration and analysis: ontology alignments, network integration and analysis, and drug-target interaction network predication for drug discovery. [SC2 – SC4]

·        Designing algorithms of identifying set similarity. [SC2]

·        Dynamics analysis and prediction of networking data (designing and developing conflict-free scheduling algorithms that infer the dynamics of protein-protein interaction networks). [SC8, SC9]

·        Designing algorithms for comparing and aligning networks. [SC10 - SC20]

·        Metabolic pathway representation and analysis. [SC5]

·        Agent-based modeling and simulation with application in mobile sensor networks. [SC22, 23]

·        Parallel computing (developed a prototype platform (iC2mpi) for parallelizing sequential iterative programs in a grid computing system and implemented the battlefield management parallel simulation in C, MPI, and OpenMP with the hypercube NUMA architecture).  [SC21]

·        Identifying temporal network biomarkers [SC1]

PUBLICATIONS

PUBLICATIONS IN CS EDUCATION

[Edu1] Qiong Cheng. Towards Connected Modern Teaching Machine: An Agile Adaptive Learning App to Customize Learning Materials and Assessments on the Fly. 2023 ACM technical symposium on Computer science education (SIGCSE).  (Demo paper)

[Edu2] Qiong Cheng, Mahitha Garikipati, and Smirthi Meenakshisundaram. "Predicting at-risk Students to Facilitate Scaffolding Instructions." 2021 IEEE Frontiers in Education Conference (FIE).

[Edu3] Qiong Cheng, David Benton, and Andrew Quinn. “Building a Motivating and Autonomy Environment to Support Adaptive Learning“ 2021 IEEE Frontiers in Education Conference (FIE).

[Edu4] Qiong Cheng. "Enrich a data structures course with parallelism." In 2020 IEEE Frontiers in Education Conference (FIE), pp. 1-5. IEEE, 2020.

[Edu5] Qiong Cheng, Felix Lopez, and Athina Hadjixenofontos. "Integrating Introductory Data Science into Computer and Information Literacy through Collaborative Project-based Learning." In 2019 IEEE Frontiers in Education Conference (FIE), pp. 1-5. IEEE, 2019.

[Edu6] Qiong Cheng. "Enhancing Essential Data Skills for College-wide Students." In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE), pp. 1262-1262. 2019. (Poster)

[Edu7] Qiong Cheng. “Online Deep Learning to Facilitate a Flipped and Adaptive Classroom on the Fly” In 2023 IEEE Frontiers in Education Conference (FIE)

[Edu8] Qiong Cheng, Harini Ramaprasad, Edward Fleming, Sharad Swaminathan, and Rahul Das. “Comparison of the Effectiveness of Company-sponsored versus Student-selected Project-based Learning in Online Database Classes” In 2024 IEEE Frontiers in Education Conference (FIE)

[Edu9] Samhith Dara, Qiong Cheng. “Evaluating Handwritten and Multimodal, Free-Style Responses in Algorithms and Data Structures: a RAG-LLM-Based Feedback Framework”. In 2025 IEEE Frontiers in Education Conference (FIE)

[Edu10] Zane Hutchens, Qiong Cheng. “RAG-LLM Ensembled Framework for Automated Domain-Specific Concept Graph Extraction”. In 2025 IEEE Frontiers in Education Conference (FIE)

[Edu11] Rahul Das, Qiong Cheng. “GraphRAG-Augmented Mastery Modeling for Student Programming Learning Trajectory Prediction and Explanation via Fuzzy Clustering”. In 2025 IEEE International Conference on Data Mining (ICDM) UGHS symposium

PUBLICATIONS RELATED TO SCIENTIFIC COMPUTING

[SC1] Qiong Cheng.  Identifying Temporal Biomarkers of Disease Development through a Thermodynamics-enriched Ensemble Framework. 2022 Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

[SC2] Q. Cheng, S. Mehta, S. Schurer. (2018) A Gene Family-led Meta-Analysis of Drug-Target Interactions. The 3rd International Workshop on Semantics-Powered Data Analytics (SEPDA 2018), in conjunction with the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018)

[SC3] Q. Cheng, et al. The ontology reference model for visual selectivity analysis in drug-target interactions. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017.

[SC4] Q. Cheng, O. Ursu, T. Oprea, S. Schurer. Learning reference-enriched approach towards large scale active ontology alignment and integration. In2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2017 Nov 13 (pp. 1658-1663). IEEE.

[SC5] Temate-Tiagueu Y, Al Seesi S, Mathew M, Mandric I, Rodriguez A, Bean K, Cheng Q, Glebova O, Măndoiu I, Lopanik NB, Zelikovsky A. (2016). Inferring metabolic pathway activity levels from RNA-Seq data. BMC genomics, 17(5), 542.

[SC6] Q. Cheng, M. Kazemian, H. Pham, C. Blatti, S. E. Celniker, S. A. Wolfe, M. H. Brodsky, S. Sinha. (2013) Computational identification of diverse mechanisms underlying transcription factor-DNA occupancy. PLoS Genet 9(8): e1003571. doi:10.1371/journal.pgen.1003571 (Impact Factor: 8.69) (voted in "top 10 papers in regulatory genomics for 2012-13" at https://www.iscb.org/recomb-regsysgen2013-program/recomb-regsysgen2013-top10-paper-awards)

[SC7] M. S. Enuameh, Y. Asriyan, A. Richards, R. G. Christensen,V. L. Hall, M. Kazemian, C. Zhu, H. Pham, Q. Cheng, C. Blatti, J. A. Brasefield, M. D. Basciotta, J. Ou, J. C. McNulty, L. J. Zhu, S. E. Celniker, S. Sinha, G. D. Stormo, M. H. Brodsky, S. A. Wolfe. (2013) Global analysis of Drosophila Cys2-His2 zinc finger proteins reveals a multitude of novel recognition motifs and binding determinants. Genome Res. 2013 Jun;23(6):928-40. doi: 10.1101/gr.151472.112. Epub 2013 Mar 7. (Impact Factor: 13.608)

[SC8] Q. Cheng, M. Ogihara, and V. Gupta. (2011) Learning Condition-Dependent Dynamical PPI Networks from Conflict- Sensitive Phosphorylation Dynamics. Proceedings of IEEE International conference on Bioinformatics and Biomedicine (BIBM), 309-312.

[SC9] Q. Cheng, M. Ogihara, and V. Gupta. (2011) Inferring Conflict-Sensitive Phosphorylation Dynamics. Proceedings of ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB), 430-434.

[SC10] Q. Cheng, P. Berman, R. Harrison, and A. Zelikovsky. (2013) Efficient Alignments of Metabolic Networks with Bounded Treewidth. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics. Wiley Book Series on Bioinformatics, 2013, pp. 413-430.

[SC11] Q. Cheng and A. Zelikovsky. (2011) Combinatorial Optimization Algorithms for Network Alignments and Their Applications. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 2(1), 1-23. 

[SC12] Q. Cheng, P. Berman, R. Harrison, I. Mandoiu, and Alex Zelikovsky. (2010) Efficient Alignments of Metabolic Networks with Bounded Treewidth.  ICDM 2010 workshop on Biological Data Mining and its Applications in Healthcare (ICDMW), 687-694.

[SC13] Q. Cheng, J. Wei, A. Zelikovsky and M. Oghara. (2010) Fixed-Parameter Tractable Combinatorial Algorithms for Metabolic Networks Alignments. ICDM 2010 workshop on Biological Data Mining and its Applications in Healthcare (ICDMW), 679-686.  

[SC14] Q. Cheng, M. Ogihara, J. Wei, and A. Zelikovsky. (2010) WS-GraphMatching: A Web Service Tool for Graph Matching. Proceedings of 19th ACM Conference on Information and Knowledge Management (CIKM), Demo, 1949-1950.

[SC15] Q. Cheng, R. Harrison, and A. Zelikovsky. (2009) MetNetAligner: a web service tool for metabolic network alignments. Bioinformatics (2009) 25 (15): 1989-1990. (Impact factor: 5.468)

[SC16] Q. Cheng (2009) Network Alignments and their Applications. PhD Dissertation

[SC17] Q. Cheng, A. Zelikovsky. (2009) Network Mapping of Metabolic Pathways. Analysis of Complex Networks: From Biology to Linguistics, Wiley-VCH, ISBN 978-3-527-32345-6, 271-293.

[SC18] Q. Cheng, P. Berman, R. Harrison and A. Zelikovsky. (2008) Fast Alignments of Metabolic Networks. Proceedings of IEEE International conference on Bioinformatics and Biomedicine (BIBM), 147-152. 

[SC19] Q. Cheng, D. Kaur, R. Harrison, and A. Zelikovsky. (2007) Mapping and Filling Metabolic Pathways. RECOMB Satellite Conference on Systems Biology.

[SC20] Q. Cheng, R. Harrison, and A. Zelikovsky. (2007) Homomorphisms of Multisource Trees into Networks with Applications to Metabolic Pathways. Proceedings of IEEE 7-th International Symposium on BioInformatics and BioEngineering (BIBE), 350-357

[SC21] H. Botadra, Q. Cheng, S. K. Prasad, E. Aubanel and V. Bhavsar. (2007) iC2mpi : A Platform for Parallel Execution of Graph-Structured Iterative Computations. Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC) in conjunction with 21th International Parallel and Distributed Processing Symposium (IPDPS), 1-8.

[SC22] Q. Cheng, Y. Zhang, X. Hu, N. Hundewale, A. Zelikovsky. (2006) Routing Using Messengers in Sparse and Disconnected Mobile Sensor Networks. Proceedings of Atlantic Web Intelligence Conference (AWIC), 31-40

[SC23] N. Hundewale, Q. Cheng, X. Hu, A. Bourgeois and A. Zelikovsky. (2006) Autonomous Messenger Based Routing in Disjoint Clusters of Mobile Sensor Networks.  Proceedings of Spring Simulation Multiconference (SpringSim) pp. 57-64.

[SC24] Ellen Wei, Harshini Bulusu, Wenwu Tang, and Qiong Cheng. 2025. Measuring Effects of Caffeine and Melatonin on Learning Trends of Danio rerio Juveniles Using Smart Sensing. The Journal of Emerging Investigators (Accepted)

[SC25] Swetha Balaji, Rowan Amanna, Ellen Wei, and Qiong Cheng. 2025. Towards Robust Anomaly Detection in Fish Behavior: Hybrid LLM–ML Ensembles and Federated Learning. In 2025 IEEE International Conference on Data Mining (ICDM) UGHS symposium. (Accepted)

HONORS, AWARDS, OR FUNDING SUPPORT

 2024-2025 Faculty Research Grant (UNCC FRG), titled “High-performance Computing for Interconnected Digital Twins to Recommend Dynamic Team Strategies"

 2023-2024 Scholarship of Teaching and Learning (UNCC SOTL) Grant, titled " Odonata: Adaptive to Promote Equity and Diversity in Computer Science Education"

 CS Education Innovation Grants (2021 Internal)

 F21 preceptor funding (2020 Fall, 2021 Spring/Fall, 2022 Spring/Fall, 2023 Spring)

 Parallel and Distributed Computing Education Internal Fund at UNCC (2019 with Dr. Erik Saule and Dr. Dong Dai)

 Parallel and Distributed Computing Education External Fund (2019 - 2020)

 Faculty Teaching Development Award (inner support from NSF funded Connected Learner project investigators) (2019 - 2020)

 Top 40 academy, tier 1 and 2 (2020)

 CompEd travel award (2019)

 SIGCSE travel award (2019)

 One of my papers was nominated as “Top ten paper award in the field of Regulatory and Systems Genomics, 2012-13”

http://www.iscb.org/recomb-regsysgen2013-program/recomb-regsysgen2013-top10-paper-awards

·     Mathematical Biology and Numerical Analysis workshop travel award, 2009

·     GSU Molecular Basis of Disease Fellowship, 2007 - 2009

·     RECOMB Satellite Conference on Systems Biology travel award, 2007

·     BIBE travel award, 2007

·     Engineer of Excellence, 2004

·     Award for Excellence and Creativity 1996

·

COURSE TAUGHT

Course ID           Level           Title

ITCS 2214 Undergraduate Intro. to Data Structures and Algorithms

ITCS 3145 Undergraduate Intro. to Parallel and Distributed Computing

ITCS 3160 Undergraduate Intro. to Database Design and Implementation

ITCS 3162 Undergraduate Intro. to Data Mining

ITCS 3190 Undergraduate Intro. to Cloud Computing for Data Analysis

ITSC 4990 Undergraduate Independent Study

ITCS 5145 Graduate Parallel Computing

ITCS 6114 & 8114 Graduate Algorithms and Data Structures

ITCS 6160 & 8160 Graduate Database Systems

ITCS 6880/ 6882 Graduate Independent Study

CIS 4617 Undergraduate Knowledge Management

CIS 4347 Undergraduate Information Storage and Management

COP 4656 Undergraduate Mobile Application Development

COP 4834 Undergraduate Data-driven Web Application

COP 4723 Undergraduate Database Administration

COP1334 Undergraduate Intro. to C++ Programming

COP 2335 Undergraduate Advanced C++ Programming

COP 2800 Undergraduate Java Programming

CGS 1060C Undergraduate Introduction to Computer Technology and Applications

CSC 3320 Undergraduate System-level Programming

CSC 1010 Undergraduate Intro. to Computer Science

SERVICES

STUDENT MENTORING

Undergraduates

Swetha Balaji – Honor Thesis in Fall 2024 and Spring 2025. Her paper titled “Towards Robust Anomaly Detection in Fish Behavior: Hybrid LLM–ML Ensembles and Federated Learning” got accepted in 2025 IEEE International Conference on Data Mining (ICDM) UGHS symposium. (Advised by Qiong)

Greyson Shafiei - Honor Thesis in Fall 2024 and Spring 2025. His thesis titled "Emotion Recognition Through GPT-4 Computer Vision Analysis of Facial Expressions” advised by Dr. Doug Markant (Psychological Science)

Rowan Amanna – Capstone Spring 2025, titled “Explainable Distributed Machine Learning for Outlier Detection” and advised by Qiong. (Co-authored “Towards Robust Anomaly Detection in Fish Behavior: Hybrid LLM–ML Ensembles and Federated Learning” got accepted in 2025 IEEE International Conference on Data Mining (ICDM) UGHS symposium” (Accepted))

Zane Hutchens – Capstone Spring 2025, titled “Using Large Language Models to Extract Knowledge from Textbook” and advised by Qiong. First-authored “WIP: RAG-LLM Ensembled Framework for Automated Domain-Specific Concept Graph Extraction” published in 2025 IEEE Frontiers in Education Conference (FIE).

Rahul Das – Undergraduate researcher Summer 2024, Spring 2025, “Learning Analysis”

Graduates

Zane Hutchens – Independent Study Fall 2025, titled “Domain-Specific Knowledge Graph Evolving”

Akhil Immadi – Independent Study Fall 2025, titled “Facilitate students with knowledge graph-enhanced learning by doing”

SELECTED EXTERNAL SERVICES

Two NSF Proposal Review Panels 12/24 - 05/25

Reviewed 26 proposals and participated in panel discussions

Judge of GRS at UNCC (2024)

ITiCSE program committee (2021, 2022, 2023, 2024)

Reviewer of FIE (2020, 2021, 2022, 2023, 2024, 2025)

Reviewer of SIGCSE Virtual (2024)

Reviewer of SIGCSE (2020, 2021, 2024, 2025)

Judge of Congressional App Challenge (2020, 2021, 2022, 2024, 2025)

University Graduate School Summer Fellows (GSSF) Reviewer (2022)

CSAE 2022 (The 6th International Conference on Computer Science and Application Engineering) Reviewer

Session Moderator of FIE (2020)

Member of the Wake Forest Baptist Center for Biomedical Informatics (WFBMI) (12/2018 – 04/2024)

Reviewer of Journal of BMC Bioinformatics (supplements)

Reviewer of BMC Special Issue devoted to ICCABS (2015)

Reviewer of BMC Systems Biology, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), International Journal of Bio-Inspired Computation (IJBIC), Journal of Intelligent Learning Systems and Applications, and Journal of Computers in Biology and Medicine

Reviewer of ICWS2010, 19th Annual International Conference on Intelligent Systems for Molecular Biology, the 13th Annual International Computing and Combinatorics, and the First International Conference on Combinatorial Optimization and Application.

UNIVERSITY/COLLEGE/DEPARTMENT SERVICES AT UNCC

Teaching Faculty Search Committee (2025)

Teaching Faculty Search Committee Chair (2023-2024): Qiong coordinated committee members to draft position description in Fall 2023 and will organize candidate interviews. This service has potential to help our departmental educational research.

College Undergraduate Committee 2024-2025

Core-curriculum Committee in College 2022-2023

Departmental Undergraduate Committee 2023-2024

Data Science Concentration Committee in Department 2023-2024

Graduate Committee in Department 2022-2023

ICC faculty in College 05/2018 – 12/2024

Academic Advising in College 08/2018 – 12/2023

Teaching reviewer in Department 01/2020 – 05/2024

Teaching reviewer committee chair 2021-2022

Graduate Thesis committee 2020

Instructor of graduate/undergraduate independent study (2021, 2019)

Top 40 academy, tier 1 and 2 (2020)

Outreach service: annual Charlotte Kids’ Festival (2018, 2019)

Volunteer in the commencement on Dec. 15, 2018.

DIVERSITY AND BROAD PARTICIPATION

·     Organizing a computing club in a nearby public school (2021-2022)

·     Worked as a judge of Congressional App Challenge (2020, 2021, 2024)

·     Coached in Family Code Night at Myers Park Traditional School (MPTS) (2019)

·     Organized a booth in Charlotte Kids Festival (2018, 2019)

·     Organized students to attend the Third Annual Systems Biology Data Science Symposium (2018)

·     Organized students to attend Data Science Salon conference (Miami) (2018)

·     Present in the Faculty Works Showcase in the Annual Miami Book Fair (2017)

·     Organized students to attend the Second Annual Big Data Conference at the University of Miami (2017)

·     Organized the ‘Data Analytics Camp’ event (2017)

·     Organized ‘Big Data’ one-day camp (2017)

·     Present in the annual Wolfson faculty convocation at the Miami Dade College (2017)

·     Organized course work session (2017)