Fall 2026, Wednesdays 2 to 3:20 pm, NCS 220 and Zoom link to be announced soon.

The seminar will be jointly taught by Prof. Dimitris Samaras (samaras@cs.stonybrook.edu).

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision.

To enroll in this course, you must either: (1) be in the Ph.D. program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome!

Abstract: The faster AI automation spreads through the economy, the more profound its potential impacts, both positive (improved productivity) and negative (worker displacement). The previous literature on AI Exposure cannot predict this pace of automation since it attempts to measure an overall potential for AI to affect an area, not the technical feasibility and economic attractiveness of building such systems. In this work, we present a new type of AI task automation model that is end-to-end, estimating: the level of technical performance needed to do a task, the characteristics of an AI system capable of that performance, and the economic choice of whether to build and deploy such a system. The result is a first estimate of which tasks are technically feasible and economically attractive to automate - and which are not. We focus on computer vision, where cost modeling is more developed. We find that at today's costs U.S. businesses would choose not to automate most vision tasks that have AI Exposure, and that only 23% of worker wages being paid for vision tasks would be attractive to automate. This slower roll-out of AI can be accelerated if costs fall rapidly or if it is deployed via AI-as-a-service platforms that have greater scale than individual firms, both of which we quantify. Overall, our findings suggest that AI job displacement will be substantial, but also gradual - and therefore there is room for policy and retraining to mitigate unemployment impacts.

Details of this work can be found here.

Speaker Bio: Neil Thompson is the Director of the FutureTech research project at MIT's Computer Science and Artificial Intelligence Lab and a Principal Investigator at MIT's Initiative on the Digital Economy.

Previously, he was an Assistant Professor of Innovation and Strategy at the MIT Sloan School of Management, where he co-directed the Experimental Innovation Lab (X-Lab), and a Visiting Professor at the Laboratory for Innovation Science at Harvard. He has advised businesses and government on the future of Moore's Law, has been on National Academies panels on transformational technologies and scientific reliability, and is part of the Council on Competitiveness' National Commission on Innovation & Competitiveness Frontiers.

He has a PhD in Business and Public Policy from Berkeley, where he also did Masters degrees in Computer Science and Statistics. He also has a masters in Economics from the London School of Economics, and undergraduate degrees in Physics and International Development. Prior to academia, He worked at organizations such as Lawrence Livermore National Laboratory, Bain and Company, the United Nations, the World Bank, and the Canadian Parliament.

Location: IACS Seminar Room
Abstract: Survival prediction using whole slide images (WSIs) can be formulated as a multiple instance learning (MIL) problem. However, existing MIL methods often fail to explicitly capture pathological heterogeneity within WSIs, both globally -- through long-tailed morphological distributions, and locally through -- tile-level prediction uncertainty. Optimal transport (OT) provides a principled way of modeling such heterogeneity by incorporating marginal distribution constraints. Building on this insight, we propose OTSurv, a novel MIL framework from an optimal transport perspective. Specifically, OTSurv formulates survival predictions as a heterogeneity-aware OT problem with two constraints: (1) global long-tail constraint that models prior morphological distributions to avert both mode collapse and excessive uniformity by regulating transport mass allocation, and (2) local uncertainty-aware constraint that prioritizes high-confidence patches while suppressing noise by progressively raising the total transport mass. We then recast the initial OT problem, augmented by these constraints, into an unbalanced OT formulation that can be solved with an efficient, hardware-friendly matrix scaling algorithm. Empirically, OTSurv sets new state-of-the-art results across six popular benchmarks, achieving an absolute 3.6% improvement in average C-index. In addition, OTSurv achieves statistical significance in log-rank tests and offers high interpretability, making it a powerful tool for survival prediction in digital pathology.

Speaker: Qin Ren

Location: https://stonybrook.zoom.us/j/97458849045?pwd=SREklaxa7EFhKaK6TeRXgQPH64vrQJ.1&jst=2

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning.

ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics.

ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.


For more information and registration, visit the official website.























A new virtual seminar series on Games, Decisions, and Networks will start this Friday. The series aims at bringing together researchers working on foundations and applications of games theory, decision theory, and networks from computer science, control, economics and operation research. 





The advisory board for the series comprises Asu Ozdaglar (MIT), Christos Papadimitriou (Columbia), Drew Fudenberg (MIT), Eva Tardos (Cornell), Matthew O. Jackson (Stanford), Ramesh Johari (Stanford), and Tamer Başar (UIUC). 
The first talk will be given by Costantinos Daskalakis (MIT) on January 22nd at noon ET, titled Equilibrium Computation and the Foundations of Deep Learning. Upcoming speakers include


- Rakesh Vohra (Upenn)
- Sanjeev Goyal (Cambridge)
- Aaron Roth (Upenn)
- Aislinn Bohren (Upenn)
- Jason Marden (UCSB)

and more to be added!
Abstract: The recent expansion of online sport wagering and igaming has led to higher rates of problem gambling, particularly among emerging adults and other population subgroups. The Center for Gambling Studies (CGS) at the Rutgers University, School of Social Work, is using big data analysis, machine learning and GIS mapping to identify geographic locations with populations most at risk to guide the development of targeted interventions. This presentation will review the GIS StoryMap for the State of New Jersey, including a blueprint for the highest risk target service areas in the state. It will also present findings from a machine learning model that identifies the key risk factors for high-intensity online casino bettors. Implications for prevention, treatment and policy initiatives will be discussed.

Bio: Lia Nower, J.D., Ph.D., is a Distinguished Professor, Associate Dean for Research, and Director of the Center for Gambling Studies at Rutgers University. A clinician and attorney, her research focuses on big data analysis and machine learning models for online gambling and sports wagering; gambling and video gaming among emerging adults; policy initiatives around harm reduction and responsible gambling, and etiology and treatment of problem gambling. Dr. Nower serves as a senior editor for Addiction. She has received both the Research (2019) and the Lifetime Research Award (2022) from the National Council on Problem Gambling and the Board of Trustees Award for Research (2022) from Rutgers University.

Join Zoom Meeting: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09
Meeting ID: 956 1719 7636 Passcode: 924293
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Register here: https://stonybrook.zoom.us/meeting/register/RD94cHiHRwCj6xNkCZqNEg
CSE 600 Talk: Squeezing Software Performance via Eliminating Wasteful Operations presented by Xu Liu

ABSTRACT: Inefficiencies abound in complex, layered software. A variety of inefficiencies show up as wasteful memory operations, such as redundant or useless memory loads and stores. Aliasing, limited optimization scopes, and insensitivity to input and execution contexts act as severe deterrents to static program analysis. Microscopic observation of whole executions at instruction- and operand-level granularity breaks down abstractions and helps recognize redundancies that masquerade in complex programs. In this talk, I will describe various wasteful memory operations, which pervasively exist in modern
software packages and expose great potential for optimization. I will discuss the design of a fine-grained instrumentation-based profiling framework that identifies wasteful operations in their contexts, which guides nontrivial performance improvement. Furthermore, I will show our recent improvement to the profiling framework by abandoning
instrumentation, which reduces the runtime overhead from 10x to 3% on average. I will show how our approach works for native binaries and various managed languages such as Java, yielding new performance insights for optimization.

BIO: Xu Liu is an assistant professor in the Department of Computer Science at College of William & Mary. He obtained his PhD from Rice University in 2014 and joined the College of William & Mary in the same year. Prof. Liu works on building performance tools to pinpoint and optimize inefficiencies in HPC code bases. He has developed several open-source profiling tools, which are used worldwide at universities, DOE national laboratories and industrial companies. Prof. Liu has published a number of papers in high-quality venues. His papers received Best Paper Award at SC'15, PPoPP'18, PPoPP'19 and ASPLOS'17 Highlights, as well as Distinguished Paper Award at ICSE'19. His recent ASPLOS'18 paper has been selected as ACM SIGPLAN Research Highlights in 2019 and nominated for CACM Research Highlights. Prof. Liu is the receipt of 2019 IEEE TCHPC Early Career Researchers Award for Excellence in High Performance Computing. Prof. Liu served on the program committee of conferences such as SC, PPoPP, IPDPS, CGO, HPCA and ASPLOS.
Join librarian Christine Fena for an interactive workshop that invites you to explore AI tools firsthand, not just as users, but as critical investigators. Through playful experimentation and collaborative discovery, you'll uncover inherent biases, probe algorithmic flaws, and gain a deeper understanding of AI's limitations and societal impacts.

Register for the Zoom workshop here.