AI & Data Science Stars Seminar Series

View Videos of Past Spring 2025 Speakers

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.

 Fall 2025 Speakers

My T. Thai is a Research Foundation Professor, Nelms Endowed Chair Professor, and Associate Director of the Nelms Institute for the Connected World at the University of Florida. An IEEE Fellow and globally recognized leader in trustworthy AI and optimization, her research addresses critical challenges in today’s interdependent, complex systems and high-impact applications such as blockchain, network infrastructure, and healthcare. She has authored 7 books and over 350 publications, earning multiple best paper awards and shaping both the theory and practice of network science and machine learning.

Responding to the global call for responsible and safe AI, Prof. Thai is a pioneer in developing deep explanations for black-box ML models and defending against explanation-guided attacks—work recognized with the 2023 AAAI Distinguished Paper Award. That same year, she received the Web Science Trust Test-of-Time Award for her landmark work on combating misinformation in social media. Her earlier honors include the IEEE Big Data Security Women of Achievement Award (2022), the DTRA Young Investigator Award (2009), and the NSF CAREER Award (2010).

Prof. Thai has served as Program Chair for several leading IEEE conferences. She is currently Editor-in-Chief of ACM Computing Surveys—ranked #1 in Computer Science, Theory & Methods—and of the Springer Journal of Combinatorial Optimization, as well as editor of Springer’s Optimization and Its Applications book series.

Spring 2025 Speakers

Click here to sign up 

Brief Bio:

Chinmay Hegde is an Associate Professor in the Tandon School of Engineering at NYU, jointly appointed with the CSE and ECE Departments. His research focuses on foundational aspects of machine learning (such as reliability, robustness, efficiency, and privacy). He also works on diverse applications of AI in engineering domains ranging from computational imaging, materials design, plant science, and cybersecurity. He is a recipient of the National Science Foundation's CAREER and CRII awards, two teaching awards, and ICML, SPARS, and MMLS best paper awards.

Abstract

GenAI tools that can mimic the appearance of people, scenes, and objects are now ubiquitous and cheap. For a variety of reasons, society urgently needs automated techniques that can distinguish between real versus fake images. One class of approaches for detecting AI-generated content relies on watermarking, which traces its roots to classical signal processing and forensics. In this talk, I will first revisit the basics of image watermarking. I will then describe two new watermarking approaches. The first approach (WIND) involves a distortion-free approach that embeds imperceptible, group-wise Fourier-domain watermarks using pseudorandom sampling. The second (SEAL) further improves robustness against forgery attacks by conditioning the watermark detection on the original images' semantic content. I will conclude with possible future directions.

The series is open to the public; no registration fee is required.

Lecture The Importance of Data Science for Cyber Security Michael Geraghty, Director of the New Jersey Cybersecurity Wednesday, Apr 9, 2025 (2:30PM – 3:30PM)  Location: GITC 2121

Click here to sign up 

Brief Bio:

Michael Geraghty is the State of New Jersey’s Chief Information Security Officer (CISO) and Director of the New Jersey Cybersecurity and Communications Integration Cell (NJCCIC). In these roles Director Geraghty is responsible for the development and execution of the State’s cybersecurity strategy. He is responsible for leading and coordinating New Jersey’s cybersecurity efforts while building resiliency throughout the State and has direct responsibility for all aspects of statewide cybersecurity operations; governance, risk and compliance; and incident response. Mr. Geraghty is an accomplished cybersecurity executive with a history of building innovative and model programs in private and public sector enterprises including roles as CISO of the Hudson’s Bay Company, Chief Information Officer of the National and International Centers for Missing and Exploited Children, Vice President of High Technology Investigations at Prudential Financial, and Network Intrusion Detection Manager, Lucent Technologies/Bell Labs. Mr. Geraghty began his career with the New Jersey State Police, where he served 12 years and led the formation and development of its High Technology Crimes Investigations Unit. He has provided expert testimony before the United States Congress and in federal, state, and international courts on computer crime investigations and forensics. Geraghty is a past president of the Northeast Chapter of the High Technology Crimes Investigation Association and has held leadership roles in the National Strategic Policy Council on Cyber and Electronic Crime.

The series is open to the public; no registration fee is required.

Lecture Automatic Discovery of Algorithms and Neural Architectures in Scientific Machine Learning George Em Karniadakis, Brown University April 7, 2025, 1 PM - 2 PM Location: GITC 2121

Click here to sign up 

Brief Bio:

George Em Karniadakis is the Charles Pitts Robinson and John Palmer Barstow
Professor of Applied Mathematics, Brown University; also with MIT & PNNL; a
member of the National Academy of Engineering and a Vannevar Bush Faculty
Fellow. He received M.S. and Ph.D. from MIT in 1987. He was a Lecturer in the
Department of Mechanical Engineering at MIT, and subsequently he joined the Center
for Turbulence Research at Stanford/Nasa Ames, then Princeton University as
Assistant Professor in the Department of Mechanical and Aerospace Engineering and
as Associate Faculty in the Program of Applied and Computational Mathematics. He
was a Visiting Professor at Caltech in 1993 and joined Brown University as Associate
Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After
becoming a full professor in 1996, he continued to be a Visiting Professor and Senior
Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018-),
Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow
of the American Physical Society (APS, 2004-), Fellow of the American Society of
Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute
of Aeronautics and Astronautics (AIAA, 2006-). He received the SES GI Taylor
Medal (2024), the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt award
in 2017, the SIAM Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US
Association in Computational Mechanics. 

Abstract

We will first review deep neural operators, which we will use as foundation models forscientific machine learning tasks. Then, we will design two classes of ultra-fast meta-solvers for linearsystems arising after discretizing PDEs by combining neural operators with either simple iterative solvers,e.g., Jacobi and Gauss-Seidel, or with Krylov methods, e.g., GMRES and BiCGStab, using the trunk basisof DeepONet as a coarse preconditioner. The idea is to leverage the spectral bias of neural networks toaccount for the lower part of the spectrum in the error distribution while the upper part is handled easilyand inexpensively using relaxation methods or fine-scale preconditioners. We create a pareto front ofoptimal meta-solvers using a plurality of metrics, and we introduce a preference function to select the bestsolver most suitable for a specific scenario. This automation for finding optimal solvers can be extended to neural architectures for predicting time series as well as to nonlinear systems and other setups, e.g. finding the best meta-solver for space-time in time-dependent PDEs.

The series is open to the public; no registration fee is required.

Lecture Convincing Experts to (not) Trust ML Models Eric Wong, University of Pennsylvania March 14th, 2025, 11:30 am- 12:30pm  Location: GITC 2121

Click here to sign up 

Brief Bio:

Eric Wong is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. He researches the foundations of robust systems, building on elements of machine learning and optimization to debug, understand, and develop reliable systems. He is a recipient of an NSF Early Career award and an AI2050 Early Career award. 

Abstract

ML systems have a long history of being unreliable---should we trust these models today? In this talk, we will discuss the challenges and opportunities facing trustworthy machine learning. On the one hand, ML systems are prone to manipulation, as exemplified by our research on jailbreaks for large language models. We will show how this procedure can be generalized beyond safety to automatically find the weaknesses of large language models, a procedure called task elicitation. Conversely, ML systems may contain a gold mine of information waiting to be discovered, but conventional explanations lack reliability. We will show how to create explanations with practical yet provable guarantees in the language of experts, with applications in assisting real-time surgery and enabling new discoveries in cosmology.

The series is open to the public; no registration fee is required.

Lecture The Usefulness of Fibonacci Codes Shmuel T. Klein, Bar-Ilan University Time: March 17th, 2:30pm - 3:30PM.  Location: GITC 2121

Click here to sign up 

Brief Bio:

Shmuel T. Klein is a Professor Emeritus and former Head of the Computer Science Department at Bar-Ilan University, near Tel Aviv. His main research interest is in Data Compression. He has published 2 books, more than 100 scientific papers, and several patents.

The Usefulness of Fibonacci Codes

The talk explores the sub-field of Data Science, considering the algorithmic aspects of data compression and coding. Several properties and applications of Fibonacci codes are presented. These are fixed codeword sets, using binary representations of integers based on the Fibonacci sequence rather than on powers of 2. Applications range from robust data compression, over faster modular exponentiation to boosting the compression performance of rewriting codes for flash memory. No previous knowledge is assumed.

The series is open to the public; no registration fee is required.

Inaugural Lecture Emerging Legal Frameworks for AI David Opderbeck 3 p.m. Thursday, February 20th, 2025 Location: GITC 2121

Click here to sign up 

Brief Bio:

David Opderbeck is Professor of Law and Co-Director of the Gibbons Institute of Law, Science & Technology and Institute for Privacy Protection at Seton Hall University Law School. His legal scholarship focuses on artificial intelligence, cybersecurity, data privacy, and intellectual property law. He develops and teaches innovative courses in technology law, including Cybersecurity Law and Policy, Artificial Intelligence and the Law, and a Data Privacy and Security Lab. He also leads the Law School's Data Privacy and Security Compliance Program. In the core law school curriculum, he has taught Property Law, Constitutional Law, and Torts. He is also a Faculty Associate with the Berkman-Klein Center for Internet & Society at Harvard University.  Prior to his career in academia, Professor Opderbeck was a Partner in the Intellectual Property / Technology practice at McCarter & English, LLP, where he began practicing cyber and intellectual property law in the early years of the public Internet.

Emerging Legal Frameworks for AI

There is general consensus about some basic principles of AI ethics and policy, including transparency, fairness, explainability, privacy, security, and accountability.  It is unclear how these broad goals could be incorporated into positive law.  The European AI Act is the most prominent and extensive example of AI-specific law.  It embodies a regulatory framework that is in many ways similar to the EU's General Data Protection Regulation (GDPR), which previously set the global pace for comprehensive privacy regulation.  A number of U.S. states have enacted or are in the process of enacting AI laws, which are mostly issue- or sector-specific.  During the Biden Administration, the U.S. Federal government began to develop policy positions relating to AI, but there has not yet been any sustained movement towards comprehensive legislation, and the incoming Trump Administrations priorities relating to AI are unclear.  This talk will survey the existing legal landscape and highlight some difficulties policymakers face in this domain. 

Learn more about our graduate programs:

M.S. in Data Science
Ph.D. in Data Science
M.S. in Artificial Intelligence

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