University Libraries Present: Analyzing quantitative data can feel overwhelming without the right tools. In this workshop, SBU Libraries' Data Literacies Lead, Ahmad Pratama will show you how to master the basics of exploratory data analysis for quantitative data using Python. This workshop covers several techniques to help you uncover patterns and insights in your datasets.

Online RSVP via link: https://stonybrook.zoom.us/meeting/register/vEPycmDrQoGjFqkmsYHgxw
Stony Brook University Northern California Alumni Chapter - Institute for AI-Driven Discovery and Innovation Panel

Join us for a Northern California Alumni and Friends luncheon followed by a panel discussion, celebrating the Institute for AI-Driven Discovery and Innovation, moderated by Fotis Sotiropoulos, Dean, College of Engineering and Applied Sciences.

Panel Discussion with:
Richard Bravman '78, Chief Strategy Officer, Affinity Solutions
Jalal Mahmud, PhD '08, Master Inventor, IBM Watson
Reza Raji '86, CEO, Xenio Systems
Andrew Protter, PhD '83, Chief Scientific Officer, Auansa Inc.

Moderated by:
Fotis Sotiropoulos, Dean, College of Engineering and Applied Sciences

Click here for more information and to register.
Abstract: Artificial intelligence (AI) is rapidly transforming scientific discovery, enabling breakthroughs in areas ranging from drug discovery to modeling complex physical systems. In the life sciences, AI has traditionally been applied to prediction tasks such as classifying molecules as toxic or non-toxic, estimating drug properties, or solving partial differential equations. These discriminative models have proven powerful, but they are inherently limited to mapping existing inputs to deterministic outputs. A new wave of methods is shifting the paradigm from discrimination to generation: creating new possibilities, such as generating novel molecules or designing new drugs. By reframing AI as both a predictive and generative engine, this shift offers new pathways for accelerating discovery and innovation in life sciences at an unprecedented scale. This talk will cover several aspects of AI for Science (AI4Sci), beginning with advances in discriminative models for molecular systems and solving PDEs, and then turning to generative approaches, including diffusion models for 3D molecular generation and large language models for drug editing. Together, these developments illustrate how moving from prediction to creation is redefining what AI can contribute to science.

Bio: Wenhan Gao is a fourth-year Ph.D. student in Applied Mathematics under the supervision of Professor Yi Liu. He was also a Staff Research Scientist Intern at VISA Research, where he worked on large language models (LLMs) and multi-agent systems for commerce. Wenhan's research focuses on AI for Science (AI4Sci), with a particular emphasis on generative AI. His work looks deep into the fundamental mechanisms of AI models when applied to scientific tasks, and he strives to incorporate established scientific priors, such as symmetry, into model design. He has published papers as a first or corresponding author in leading AI and computational venues, including ICLR, ICML, NeurIPS, TMLR, ACL, and the Journal of Computational Physics. In addition to his research, Wenhan has served as a reviewer and oral session chair for top AI conferences and as a lecturer for both undergraduate and graduate courses at Stony Brook University.

Location: IACS Seminar Room or Zoom

This seminar will take place in person and online*

Join Zoom Meeting: https://stonybrook.zoom.us/j/91670093552?pwd=2EcniXqPZLTpa4ZBKRs1zAjYqs1LS0.1

Meeting ID: 916 7009 3552
Passcode: 434045
As generative AI tools become increasingly prevalent in education, their impact on collegiate writing raises important questions about creativity, academic integrity, and effective teaching practices. This panel brings together faculty and students to share perspectives on the opportunities and challenges that AI presents in an academic setting. Through an open dialogue, participants will engage in meaningful conversations, allowing for a deeper understanding of each other's viewpoints and fostering collaboration. Students and faculty will explore diverse ways AI can be used in teaching and learning and seek solutions to utilize AI writing tools ethically. This exchange aims to build a community of trust and shared knowledge, ensuring that AI's role in education is both innovative and responsible.

Register here: https://stonybrook.zoom.us/meeting/register/tJAqdOitpjIpHtDGAsGBfEb3ah0YIzhIJolN
The Provost's Lecture Series features talks by SUNY Distinguished Academy faculty members at Stony Brook University, showcasing the outstanding research and scholarship that is taking place at our institution.

Joe Mitchell

SUNY Distinguished Professor, Applied Mathematics and Statistics
Chair, Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences

A Case for Algorithms: A Computational Geometer's Perspective

Algorithms are all around us in every smart device and technology that has consumed our daily lives. As a computational geometer, I study algorithms to solve problems that involve a geometric perspective on data. I have observed that practically every technology and field of study has a need for effective algorithms involving geometric data. I reflect on some favorite algorithmic problems that are easy to visualize, but challenging to solve, and argue that the formal study of algorithms remains essential in the age of AI.

Reception to follow immediately after the talks.

Register here.

The Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop.

This workshop will focus on topics including big data analytics and machine learning with Spark, as well as deep learning.

This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event. SBU's Institute for Advanced Computational Science (IACS) is one of those satellite sites!

Location: IACS Conference Room #2

Interested applicants must first have an ACCESS ID. If you don't have the ID, please visit this page to create one: ACCESS USER REGISTRATION.


Once you have an ACCESS ID, please login (see top right here) then register here.

AI3 Seminar

Meir Feder

Professor, School of Electrical Engineering
Jokel Chair in Information Theory, School of Electrical and Computer Engineering
Tel-Aviv University

Information-Theoretic Framework for Understanding Modern Machine-Learning

Abstract:

Information Theory views learning as universal prediction under log loss, characterized through regret bounds. Unlike the classical results that considered ``small'' model classes and provided uniform regret, the proposed framework provides non-uniform, model dependent bounds utilizing an effective notion of architecture-based model complexity. This complexity is defined by the probability mass or volume of the set of all models in the vicinity of the target model \theta_0, in an informational distance. This volume might be hard to evaluate, yet by local analysis it is related to spectral properties of the expected Hessian or the Fisher Information Matrix at \theta_0, leading to tractable approximations. We argue that successful architectures possess a broad complexity range, enabling learning in highly over-parameterized model classes. The framework sheds light on the role of inductive biases, the effectiveness of stochastic gradient descent (SGD) algorithm, and phenomena such as flat minima. It unifies online, batch, supervised, and generative settings, and applies across the stochastic-realizable and agnostic regimes. Moreover, it provides insights into the success of modern machine-learning architectures, such as deep neural networks and transformers, suggesting that their broad complexity range naturally arises from their layered structure. These insights open the door to the design of alternative architectures with potentially comparable or even superior performance.

Biography:

Meir Feder received the Sc.D. degree in Electrical Engineering and Ocean Engineering in 1987 from the Massachusetts Institute of Technology (MIT) and the Woods Hole Oceanographic Institution (WHOI). After being a Research Associate and a Lecturer at MIT, he joined the School of Electrical Engineering, Tel-Aviv University in 1990, where he is the Jokel Chaired Professor and the former founding head of Tel-Aviv university center for Artificial intelligence and Data science (TAD). Parallel to his academic career, he is closely involved with the high-tech industry: he founded 5 companies, among them Peach Networks (Acq: MSFT) and Amimon (Acq:LON.VTC). Recently, with his renewed interest in machine learning and AI, he co-founded Run:ai (Acq:NVDA), a virtualization, orchestration, and acceleration platform for AI infrastructure, acquired by Nvidia to support its GPU cloud operation.

Prof. Feder received several academic and professional awards including the IEEE Information Theory Society best paper award, the Padovani lectureship, the creative thinking award of the Israeli Defense Forces, and the Research Prize of the Israeli Electronic Industry, awarded by the President of Israel. For the technology he developed in Amimon, he received the 2020 Scientific and Engineering Award of the Academy of Motion Picture Arts and Sciences (OSCAR) and was announced the principal inventor of the technology that attained the 73rd Engineering Emmy Award of the Television Academy.

Location: NCS120

Prof. Eugene A. Feinberg, from the Department of Applied Mathematics and Statistics, presents, Recent Developments in Markov Decision Processes Relevant to AI on April 4 at 4p. The talk discusses recent developments in Markov Decision Processes potentially relevant to artificial intelligence. These developments include complexity estimations for exact and approximate algorithms, decision making with incomplete information and multiple criteria, and continuity properties of optimal values and expectations. Dr. Eugene A. Feinberg is currently Distinguished Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He is an expert on applied probability, stochastic models of operations research, Markov decision processes, and on industrial applications of operations research and statistics. He has published more than 150 papers and edited the Handbook of Markov Decision Processes. His research has been supported by NSF, DOE, DOD, NYSTAR (New York State Office of Science, Technology, and Academic Research), NYSERDA (New York State Energy Research and Development Authority) and by industry. He is a Fellow of INFORMS (The Institute for Operations Research and Management Sciences) and has received several awards including 2012 IEEE Charles Hirsh Award for developing and implementing smart grid technologies, 2012 IBM Faculty Award, and 2000 Industrial Associates Award from Northrop Grumman. Dr. Feinberg is an Associate Editor for Mathematics of Operations Research and for Applied Mathematics Letters. He is an Area Editor for Operations Research Letters. Refreshments will be provided