Biomedical Data Science Center @ NYIT
Biomedical Data Science Center @ NYIT
Research projects
Research projects
Research and Research Projects
Dr. Eve Armstrong
Eve Armstrong is an assistant professor of physics at New York Tech and a research associate in the Department of Astrophysics at the American Museum of Natural History (AMNH.) She received her B.A. in Astrophysics from Columbia University and her Ph.D. in Physics from the University of California, San Diego (UCSD.) She studies information flow in nonlinear dynamical systems, focusing on high-density astrophysical environments and networks of biological neurons. She is also a theater producer and director, and develops workshops for scientists to hone their communication skills, using techniques from theatre, improvisation, comedy, and storytelling.
SCIENCE: Armstrong develops inference methodologies to complete models of nonlinear dynamical systems, given sparse measurements from those systems. Her current focus is neutrino emission from core-collapse supernovae (CCSN). A CCSN occurs when a supermassive star explodes catastrophically at the end of its nuclear-fusion-burning lifetime. This process bears upon fundamental questions regarding the Universe's building blocks. Having in hand a poorly constrained model and sparse measurements, Armstrong's research group develops an optimization-based inference methodology. Optimization is a means to solve a model given available measurements, where the measurements are assumed to be a manifestation of underlying physical dynamics. Their specific method differs from the better-known machine-learning paradigm, as it is designed for the case of extremely sparse—rather than plentiful—data. Armstrong is also active in adapting this procedure in areas outside astrophysics. In neuroscience, the system of interest is a region of the avian brain associated with the generation of birdsong, and the measurements are electrical voltage time series from individual neurons. Armstrong has also applied the procedure to an epidemiological model of COVID-19, to illustrate how uncertainty in measurements limits the ability to predict – i.e. control – the pandemic.
COMEDY AND SCIENCE COMMUNICATION: Armstrong is co-creator and co-artistic director of Reality Aside Theatre, a 501(c)3 incorporated in New York State. Her company has produced dark interactive theatre for public audiences, as well as science-themed sketch comedy for tri-state area schools. Now at New York Tech, She also leads workshops at AMNH for young scientists to develop their communication skills and comfort with an audience. Participants are scientists – undergraduates through faculty – throughout the NYC area.
Dr. Yusui Chen
Yusui Chen has been a tenure-track assistant professor of physics at New York Institute of Technology since fall 2018. He received his Ph.D. in physics from the Stevens Institute of Technology in 2015 and his B.S. in physics from Nanjing University in 2005.
Chen's research focuses on quantum information science and engineering, with distinct expertise in theoretical modeling and simulation of complex quantum systems, with an emphasis on non-equilibrium processes. Moreover, he and his collaborators have developed a master equation methodology to study the dynamics of entanglement and decoherence within multi-qubit systems, particularly in the presence of environmental noise. This study harnesses a synergy of classical computing and quantum simulation techniques. Furthermore, he has conducted comprehensive exploration into the utilization of entanglement to propel advancements in quantum metrology.
Dr. Jerry Cheng
Jerry Cheng is an assistant professor of computer science at NYIT College of Engineering and Computing Sciences. He earned his Ph.D. in statistics from Rutgers University. Before joining New York Tech, Cheng was on the faculty of the Robert Wood Johnson Medical School at Rutgers University. Prior to joining academia, he was a post-doctoral researcher at Columbia University, and he also worked at AT&T Labs.
Cheng's current research interests include big data analytics, artificial intelligence, data mining, statistical modeling, and high-performance computing. The main applications of his work lie in the areas of mobile computing, information security, and healthcare. His research has secured multiple grants from the National Science Foundation and the Army Research Office.
Dr. Claude Gagna
Claude Gagna is a professor in the Department of Biological and Chemical Sciences. He is the course director of Anatomical Sciences (e.g., Histology, Anatomy, Physiology, Embryology, and Pathophysiology) and a senior educator in lecture and labs. Gagna is a Molecular Biologist, DNA Nanotechnologist, and Human Anatomist who holds patents, and has published conference proceedings/abstracts, book chapters, review articles, case studies, methodologies, and original research articles. He received his B.S. in Biology from St. Peter's University, his M.S. in Human Anatomy (minor: Genetic Engineering) from Fairleigh Dickinson University School of Dental Medicine, and his Ph.D. from New York University-Basic Medical Sciences (Department of Human Anatomy, and Department of Biochemistry). He was a postdoctoral fellow at NYU-Basic Medical Sciences and Rutgers-NJMS.
Gagna’s research focuses on the development of next generation DNA and RNA microarrays, i.e., Canonical, and Multistranded, Alternative and Transitional Helical (C‐MATH) Nucleic Acid Microarrays: Double‐, Triple- and Four‐Stranded DNA and RNA Microarrays. They can be used for the characterization of gene functions (e.g., transitions in DNA structures), and drug discovery (e.g., drugs or biologics that inhibit gene expression). Additionally, Gagna is developing novel spatial genomic methods, i.e., Genomesorganizomics, based on canonical and non-canonical DNA and RNA structures, i.e., B-DNA, Z-DNA, triplex DNA and quadruplex DNA. He also focuses on clinical disease-based research, i.e., cancer (e.g., melanoma), dermatophytes, and xeroderma pigmentosum.
Dr. Michael Hadjiargyrou
Hadjiargyrou received his training in molecular and cell biology at the City University of New York and completed his post-doctoral fellowship at the California Institute of Technology. Prior to joining New York Tech, he served as assistant and associate professor in the Department of Biomedical Engineering at Stony Brook University as well as graduate program director and associate vice president for research. He is a member of the American Society for Bone and Mineral Research and the Federation of American Societies for Experimental Biology, and serves on the editorial Board of a number of scientific journals.
1. Transcriptional Profiling of Bone Regeneration
2. Discovery and characterization of Mustn1
3. MicroRNA mechanisms during fracture repair
Dr. Xueqing (Summer) Huang
Dr. Xueqing Huang is an Associate Professor in the Department of Computer Science at NYIT. She possesses extensive research experience in algorithm design and performance optimization for various Internet of Things (IoT) applications. Dr. Huang has a track record of over 40 peer-reviewed publications and is consistently featured in top-tier journals within her domain, such as IEEE Transactions on Networking and IEEE Transactions on Cloud Computing. With expertise in data analysis and biostatistics, she also harbors research interests in data-driven stem cell maturity evaluation and promotion, molecule discovery, and precision medicine.
1. Wireless Mobile Edge Computing: resilient vehicle networks, physical-layer security of IoT devices, crowdsourced video content generation, storage, and distribution
2. Data Science: energy management for residential buildings, stem cell study, and precision medicine
Dr. Wiliam Letsou
William Letsou earned his Ph.D. in Chemistry at the California Institute of Technology under Dr. Long Cai. His dissertation focused on the regulation of complex biological networks by noncommutative signaling molecules. Using techniques from enumerative combinatorics, he showed that temporal encoding involving a small number of regulators far surpasses the reachable space of traditional combinatorial/Boolean logic. After Caltech, Letsou moved to St. Jude Children's Research Hospital in Memphis, Tenn., where he worked in the Department of Epidemiology and Cancer Control. There he developed computational tools to look for combinations of genetic variants associated with disease risk. Letsou developed a pattern-mining algorithm to detect haplotypes—combinations of alleles on the same chromosome—enriched among individuals affected with disease. Using this method, he showed that a few top breast cancer risk SNPs are in fact poor tags for underlying rare, risk haplotypes of large effect, suggesting that the polygenic diseases may be more Mendelian than previously imagined.
Letsou's research at New York Tech aims to further the study of combinatorics in biology. With advances in parallel computing, it will be possible to study the association of hundreds of different variables with medically and biologically relevant outcomes. Equally important is developing sound statistical and physical models that make this information understandable to humans. Connecting the observed combinatorial associations with an underlying biological reality which may be noncommutative in nature is a long-term goal of his research program.
Dr. Leonidas Salichos
Biography: Dr. Salichos has a strong background in evolutionary and computational biology. While earning his M.S. in Agricultural Engineering at the Agricultural University of Athens, he developed a method that maps viral outbreaks. While working towards his M.S. in Bioinformatics at Katholieke Universiteit Leuven, he continued working on viruses by genotyping HIV strains. He earned his Ph.D. from Vanderbilt University in 2014. For his Ph.D. thesis, he developed several computational tools, including machine learning metrics to measure the internode and phylogenetic tree certainty based on conflicting phylogenetic signals. As a postdoctoral researcher at Yale University, he worked on developing algorithms that calculate the impact of driver mutations in cancer by estimating growth patterns using variant allele frequencies. He also worked on the identification of mutational patterns and signatures, tumor subclonal architecture, and expressional profiles in 2800 cancer tumors.
Modeling the spread of infectious diseases
Quantifying music and cultural evolution
Studying microbial populations across human tissues
Determining genomic regions that impact tumor progression and immunotherapy treatments
Dr. Baole Wen
Baole Wen is an assistant professor in the Department of Mathematics. Before joining New York Institute of Technology in 2022, he had been a postdoctoral assistant professor of mathematics at the University of Michigan and a Postdoctoral Fellow in the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. Baole received his Ph.D. in Applied Mathematics from the University of New Hampshire in 2015. His Ph.D. research was focused on understanding the underlying flow and transport mechanisms governing the spatiotemporally-chaotic system of porous medium convection at large Rayleigh numbers. He obtained a master's degree in fluid mechanics and a bachelor's degree in engineering mechanics from Beijing University of Aeronautics and Astronautics in 2010 and 2007, respectively.
Baole's research interests cover broad areas of applied and computational mathematics, including fluid mechanics, mathematical modeling, PDE-constrained optimization, scientific computing, model order reduction, efficient numerical algorithms, and pattern formation and nonlinear dynamics in high-dimensional spatiotemporal dynamical systems. To conduct his research, Baole employs direct numerical simulations, high-performance computing, variational and stability analyses, optimization, dynamical system theory, and experimental validation.
1. Exact coherent states in Rayleigh-Bénard convection
2. Flow and transport in porous media
3. Optimal mixing by incompressible flows
Dr. Dong Zhang
Zhang received his Ph.D. in biochemistry and molecular biology from Brandeis University and performed his postdoctoral training at Baylor College of Medicine and Harvard Medical School.
1. Target Breast Cancer and Ovarian Using Synthetic Lethal Strategy
2. Investigate Crosstalk Between DNA Damage Response and Centrosome Biogenesis
3. Investigate Molecular Mechanism of DNA Damage Checkpoint and DNA Repair
Dr. Yingtao (Jerry) Zhao
Zhao received his Ph.D. in Bioinformatics from the Chinese Academy of Sciences, where he specialized in genomics and bioinformatics, and earned his bachelor's degree in economics from the Nanjing University. Lastly, he received his postdoctoral training in epigenetics and brain disorders from the University of Pennsylvania.
Yingtao "Jerry" Zhao, Ph.D., is a biomedical scientist specializing in genomics, glycobiology, and neurobiology. His research aims to understand the molecular basis of heparan sulfate, long genes, and brain disorders. His research uses multidisciplinary approaches, including disease mouse models, genomics, glycobiology, neuroscience, bioinformatics, and molecular biology.
The Zhao laboratory is particularly interested in heparan sulfate and long genes. (1) Heparan sulfate is a sugar molecule that covers the surface of all human cells. Heparan sulfate plays an important role in the pathogenesis of multiple brain disorders, such as Alzheimer's disease, Parkinson’s disease, and Kallmann syndrome. (2) Long genes (> 100 kilobases) are specifically expressed in the brain and show unique genomic and epigenomic features. Long genes are associated with brain disorders, such as autism, amyotrophic lateral sclerosis, and Alzheimer’s disease.The long-term goal of the Zhao laboratory is to use mouse models, genomics, and epigenetics to reveal the role and molecular mechanisms of heparan sulfate and long genes in brain disorders, with a hope to eventually develop a cure.