AI Innovation Institute

This workshop synthesizes the latest research on the impact of AI usage in education so that you could make informed decisions on whether and how to use AI to facilitate your learning. You might have seen conflicting reports on whether the use of AI is good for learning. In this workshop, we are going to tease out, drawing on the latest research, which types of AI usage are beneficial or harmful for different kinds of learning. At the end of the workshop, you should walk away with more clarity on when and how to use AI for your own learning. Join PRODIG+ fellow on critical AI, Zheng Fu, in this informative workshop.

Register for this Zoom workshop.

Deep Surface MeshesPascal FuaEPFLGeometric Deep Learning has recently made striking progress with the advent of Deep Implicit Fields (SDFs). They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable 3D surface parameterization that is not limited in resolution. Unfortunately, they have not yet reached their full potential for applications that require an explicit surface representation in terms of vertices and facets because converting the SDF to such a 3D mesh representation requires a marching-cube algorithm, whose output cannot be easily differentiated with respect to the SDF parameters. In this talk, I will discuss our approach to overcoming this limitation and implementing convolutional neural nets that output complex 3D surface meshes while remaining fully-differentiable and end-to-end trainable. I will also present applications to single view reconstruction, physically-driven Shape optimization, and bio-medical image segmentation.

Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996 where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. He has (co)authored over 300 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. He often serves as program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies. 

University Libraries Presents

What new tools and extra powers are available to you through the library's subscription databases? Join faculty librarian Chris Kretz, Head of Academic Engagement, on a tour of the AI landscape from our database vendors.

Location: Melville Library, Central Reading Room, Lab A, or Online.

Register here to join.

Hear from Yvette Wohn, Ph.D., Inaugural Chair of the Department of Technology, AI and Society, for an engaging look at how artificial intelligence and emerging technologies are reshaping the way we live, work, interact, and connect, and get a sneak peek of how our upcoming programs will address this changing landscape.

Reception immediately following.

Talk Title: Knowledge-enhanced LLMs and Human-AI Collaboration Frameworks for Creativity Support

Abstract:

Large language models (LLMs) constitute a paradigm shift in Natural Language Processing and Artificial Intelligence. To build AI systems that are human-centered, I propose we need knowledge-aware models and human-AI collaboration frameworks to help them solve tasks ultimately aligning these models better with human values. In this talk, I will discuss my research agenda for human-centered AI with a case study on creativity that focuses on how to augment LMs with external knowledge, build effective human-AI collaboration frameworks as well as theoretically grounded robust evaluation protocols for measuring capabilities of NLG systems. I will begin by describing knowledge-enhanced methods for creative text generation such as metaphors. Next, I will describe how content creators can collaborate and benefit from the creative capabilities of text-to-image-based AI models. Finally, I will focus on the design and development of theoretically grounded evaluation protocols to benchmark the creative capabilities of Large Language Models in both producing as well as assessing creative text. I will end this talk by highlighting the current limitations of existing models and future directions toward building better models that will enable efficient and trustworthy human-AI collaboration systems.

Bio:

Tuhin Chakrabarty is a final-year Ph.D. candidate in the Natural Language Processing group within the Computer Science department at Columbia University. His research is supported by the Columbia Center of Artificial Intelligence & Technology (CAIT) & an Amazon Science Ph.D. Fellowship. He was also a Computational Journalism fellow at NYTimes R&D and an intern at the Allen Institute of Artificial Intelligence, Salesforce Research, and Deepmind. His research interests are broadly in Natural Language Processing, Computer Vision, and Human-Computer Interaction with a special focus on Human-Centered Methods for Understanding, Generation, and Evaluation of Creativity. His work has been recognized at top natural language processing and human-computer interaction conferences and journals such as ACL, NAACL, EMNLP, TACL, and CHI. He has been involved in organizing several workshops and tutorials at NLP conferences such Figurative Language Processing workshop at EMNLP 2022, NAACL 2024, and the tutorial on Creative Text Generation at EMNLP 2023. His work on AI and creativity has been mentioned in mainstream news media such as The Hollywood Reporter and more recently The Washington Post.

1. Techniques involving data driven inferences from multi-omics data
2. Interdisciplinary approaches to uncover aspects of tumor biology
3. Computational genomics method development, benchmarking, and validation

You are cordially invited to Stony Brook University's

FinTech Workshop

A conversation bringing together academic research and industry leaders

in financial technology

Friday, November 6, 2026

1 PM to 4:30 PM

First Floor Auditorium

New Computer Science Building

1 pm

Welcome & Keynote Introduction

Andrew C. Singer, Dean of the College of Engineering and Applied Sciences (CEAS), Stony Brook University

1:05 pm

Keynote Address:

Robert J. Frey, Founder and Director of Stony Brook's Quantitative Finance Program, renowned quantitative finance expert and investor

1:40 pm

Academic Session

• 1:20 pm: Jeffrey T. Ludwig, Mathematics Department and Director of Jump Labs, University of California, Irvine

• 1:30 pm: Lav Varshney, AI Innovation Institute (AI3), Stony Brook University

• 1:40 pm: Pawel Polak, Applied Math & Statistics (AMS), Stony Brook University

2:10 pm

Industry Session

• 2:00 PM: Roger Burkhardt, Broadridge

• 2:10 PM: Jill Scher, CBIZ Advisors

• 2:20 PM: Milind Sharma, QuantZ and QWAFAxNEW

2:40 pm

Industry-Academic Panel (Moderator: Yvette Wohn, Chair of Department of Technology, AI & Society)

• 2:40 PM: Panel discussion

• 3:00 PM: Audience Q&A

3:10 pm

Broadridge Innovation Lab

Samir Das, Chair of the Computer Science Department

3:15 pm

Coffee & Networking

Open floor until 4:30 PM

Please RSVP by Friday, October 30

Register Here