AI & Data Science Stars Seminar Series
Hosted by:
Associate Professor Hai Phan
Assistant Professor Mengjia Xu
Assistant Professor Akshay Rangamani
The AI & Data Science Stars Seminar Series aims to establish interdisciplinary collaboration by connecting emerging scholars and recent NSF (or other agencies) CAREER awardees across diverse research fields, especially targeting Data Science (DS) faculty members’ research directions and beyond (including Computer Science and Information Systems), enhancing the DS department's identity and Ying Wu College of Computing (YWCC) as a hub for innovation and thought leadership. It provides an open platform for sharing cutting-edge research, inspiring students and early-career researchers, and fostering professional networks between academia and industry.
2026 Speakers
LectureNJIT AI & Data Science Stars Seminar Series Explainability, Generation, Physics and Dynamics in ML for Computer Vision and Biomedical Applications Dimitris Metaxas January 30, 2026 |
Abstract: We have been developing a computational learning and AI framework that combines principles of physics-based deformable models, dynamical systems, domain knowledge and generative methods theory to augment the performance of pure data driven ML methods. Methods include the discovery of ODEs and the incorporation of domain knowledge to offer human level explainability and abstractions, and the ability to deal with data statistics and dynamics not in training dataset. This framework has been used for resolution of complex dynamic problems in computer vision and biomedical application. We will present results in human and multi-object tracking and dynamics, data generation, cardiac and cancer analytics and we will provide insights into learning-based decision making, and the use of generative methods. We will conclude with future research directions.
Dimitris Metaxas is a Board of Governors and Distinguished Professor in the Computer and Information Sciences Department at Rutgers University. He is directing the CBIM Center and the NSF University-Industry Collaboration Center CARTA. Dr. Metaxas has been conducting research towards the development of novel methods and technology upon which AI, machine learning, computer vision, medical image analysis, and graphics/generative methods can advance synergistically. In biomedical image analysis he developed AI, Machine Learning and deformable model-based methods for material modeling and shape estimation of internal organs from MRI, SPAMM and CT data, explainable diagnosis methods, cancer and cell, analytics. In computer vision he has focused on novel ML estimation methods including foundation models (generation, explainability, interpretability) for human behavior analytics, ASL recognition from video, scene understanding, physics-based modeling and complex dynamical systems, including autonomous driving applications. Dr. Metaxas has published over 800 research articles in these areas and has graduated over 80 PhD students, who occupy academic and industry positions. His research has been funded by NIH, NSF, AFOSR, ARO, DARPA, HSARPA, and the ONR. Dr. Metaxas work has received many best paper awards and he has 10 patents. He was awarded a Fulbright Fellowship in 1986, is a recipient of an NSF Research Initiation and Career awards, and an ONR YIP. He is a Fellow of the American Institute of Medical and Biological Engineers, a Fellow of IEEE and a Fellow of the MICCAI Society. He has been involved with the organization of all major conferences in computer vision and medical images analytics ( GC CVPR 2026, IEEE CVPR 2014, ICCV 2011, IPMI 2025, DDDAS 24 26, MICCAI 2008; PC ICCV 2007, FIMH 2011 and SCA 2007).
 | LectureNJIT AI & Data Science Stars Seminar Series FUNDAMENTAL PRINCIPLES IN DEEP LEARNING Tomaso Poggio Friday, January 23, 2026 |
Abstract: Modern deep learning works astonishingly well, yet we still lack a fundamental understanding of why. What we call “deep learning” is not merelya triumph of computer science or engineering; it is a natural phenomenon that demands explanation in terms of underlying principles. In recent work two principles emerge. The first is (sparse) compositionality, and the second is genericity. I will describe how sparse compositionality is mathematically implied by efficient computability, just as genericity is dictated by assuming invariance in the choice of coordinates of the input variables. Sparse compositionality explains why the architecture of deep networks is needed and why neural nets avoid the curse of dimensopnality; Genericity explains the
unreasonable effective-ness of gradient descent techniques in training, Together, these principles of-fer a first step toward a unified theory of modern AI—one that explains not only how
current systems work, but why they work at all.
Tomaso A. Poggio, is a physicist whose research has always been between brains and computers. It is now focused on the mathematics of deep learning and on the computational neuroscience of the visual cortex. He is the Eugene McDermott Professor in the Dept. of Brain & Cognitive Sciences at MIT and the co-director of the NSF Center for Brains, Minds and Machines (CBMM) at MIT. He is a member of the American Academy of Arts and Sciences and Fellow of the American Association for the Advancement of Science (AAAS), a founding fellow of AAAI, and a founding member of the McGovern Institute for Brain Research. Among other awards he received the 2014 Swartz Prize for Theoretical and Computational Neuroscience and the IEEE 2017 Azriel Rosenfeld Lifetime Achievement Award. A former Corporate Fellow of Thinking Machines Corporation, a former director of PHZ Capital Partners, Inc. and of Mobileye, he was involved in starting, or investing in, several other high tech companies including Arris Pharmaceutical, nFX, Imagen, Digital Persona, DeepMind, and Orcam.
 | LectureNJIT AI & Data Science Stars Seminar Series Protein data bank: From two epidemics to the global pandemic to mRNA vaccines and Paxlovid Stephen K. Burley Nov 3, 2025, 2:30PM - 3:30PM GITC 2121 |
Abstract: Structural biologists and the open-access Protein Data Bank (PDB) played decisive roles in combating the COVID-19 pandemic. Global biostructure data were turned into global knowledge, allowing scientists and engineers to understand the inner workings of coronaviruses and develop effective countermeasures. Two mRNA vaccines, initially designed with guidance from PDB structures of the SARS-CoV-1 and MERS-CoV spike proteins, prevented infections entirely or reduced morbidity and mortality for more than five billion individuals worldwide. Structure-guided drug discovery by Pfizer, Inc. (facilitated by PDB structures), initiated in the 2000s in response to SARS-CoV-1 and resumed in 2020, yielded nirmatrelvir (the active ingredient of Paxlovid) -- a potent, orally bioavailable inhibitor of the SARS-CoV-2 main protease. You’ve got to love the Protein Data Bank!
Bio: Stephen Burley is an expert in structural biology, proteomics, data science, structure/fragment-based drug discovery, artificial intelligence/machine learning, and clinical medicine/oncology. Burley currently serves as University Professor and Henry Rutgers Chair, Founding Director of the Institute for Quantitative Biomedicine, Director of the RCSB Protein Data Bank, Tenured Member of the Department of Chemistry and Chemical Biology, and Director of the Rutgers Artificial Intelligence and Data Science Collaboratory at Rutgers, The State University of New Jersey. He is also a Member of the Rutgers Cancer Institute, wherein he Co-Leads the Cancer Pharmacology Research Program. From 2008 to 2012, Burley was a Distinguished Lilly Research Scholar in Eli Lilly and Co. Before joining Lilly, Burley served as the Chief Scientific Officer and Senior Vice President of SGX Pharmaceuticals, Inc., a biotechnology company that went public on the NASDAQ in 2006 and was acquired by Lilly in 2008. Until 2002, Burley was the Richard M. and Isabel P. Furlaud Professor at The Rockefeller University and an Investigator in the Howard Hughes Medical Institute. He has authored/coauthored more than 350 scientific publications. Following undergraduate training in physics and applied mathematics, Burley received an M.D. degree from Harvard Medical School in the joint Harvard-MIT Health Sciences and Technology Program, and, as a Rhodes Scholar, received a D.Phil. in Structural Biology from Oxford University. He trained in internal medicine at the Brigham and Women's Hospital in Boston and conducted postdoctoral work with Gregory A. Petsko at the Massachusetts Institute of Technology and Nobel Laureate William N. Lipscomb, Jr. at Harvard University. With William J. Rutter and others at the University of California, San Francisco and Rockefeller, Burley co-founded Prospect Genomics, Inc., which was acquired by SGX in 2001. He is a Fellow of the Royal Society of Canada, the New York Academy of Sciences, the American Crystallographic Association, and The Protein Society, and recipient of a Doctor of Science (Honoris causa) degree from his alma mater, the University of Western Ontario, from which he graduated first in his class with a B.Sc. in Physics.
The series is open to the public; no registration fee is required.
Learn more about our graduate programs:
M.S. in Data Science
Ph.D. in Data Science
M.S. in Artificial Intelligence