TITLE: Sampling Using Langevin Diffusions Beyond the Worst-Case by Andrej Risteski of CMU


ABSTRACT: Many tasks involving generative models involve being able to sample from distributions parametrized as p(x) = e^{-f(x)}/Z where Z is the normalizing constant, for some function f whose values and gradients we can query. This mode of access to f is natural -- for instance sampling from posteriors in latent-variable models. Classical results show that a natural random walk, Langevin diffusion, mixes rapidly when f is convex. Unfortunately, even in simple examples, the applications listed above will entail working with functions f that are nonconvex.

We exhibit instances where Langevin diffusion (combined with other tools) can provably be shown to mix rapidly in instances of relevance in practice: distributions p that are multimodal, as well as distributions p that have a natural manifold structure on their level sets. 

Abstract: Computer vision seeks to extract semantic and geometric information from images and videos, serving as the perceptual foundation for intelligent systems such as robots and autonomous vehicles. Over the past decade, deep learning has driven remarkable progress in the field, advancing capabilities from 2D recognition to 3D reconstruction. However, the current purely data-driven paradigm faces fundamental challenges, including data inefficiency, curse of high dimensionality, and limited understanding of visual entities beyond individual objects.

In this talk, I will present my recent research on modeling and learning rich visual structures to address these challenges. First, I will introduce a novel framework that integrates explicit visual dependency modeling with deep learning for 2D and 3D dense prediction. Next, I will demonstrate how unfolding the manifold structure of visual data enables unsupervised semantic segmentation. Finally, I will present a recent project that represents, parses, and learns the geometric compositionality of 3D objects to facilitate self-supervised part-whole reconstruction. Through these efforts, I aim to bridge the gap between data-driven deep learning and visual structure modeling, paving the way for more efficient, generalizable, and interpretable computer vision models.

Bio: Dr. Wei Tang is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago (UIC). He obtained his Ph.D. in Electrical Engineering from Northwestern University, where his dissertation was honored with a Best Dissertation Award. His research interests include computer vision, digital image processing, and machine learning. Dr. Tang has served as an associate editor for several international journals, including Pattern Recognition and Machine Vision and Applications, and as an area chair for leading conferences, including CVPR, ICCV, and WACV. His research has been funded by the National Science Foundation (NSF) and industry partners such as Motorola and Wormpex AI Research.


Location: NCS 115

Zoom: https://stonybrook.zoom.us/j/4624091659?omn=95178138684&jst=3

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Learning Generalizable Program and Architecture Representations for Performance Modeling

Abstract: Performance modeling is an essential tool in many areas of computer science and engineering. However, existing performance modeling approaches have limitations, such as high computational cost, narrow flexibility, or restricted accuracy/generality. To address these limitations, this talk introduces PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional and independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling-related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general and efficient than previous approaches. This talk will also introduce how PerfVec's design principles can benefit broader research areas.

Biography: Lingda Li is a computer scientist at Brookhaven National Laboratory. He is generally interested in computer architecture and programming model research, with focus on simulation/modeling, memory systems, and machine learning. Before joining BNL, he worked at the Department of Computer Science of Rutgers University as a postdoc to carry out GPGPU research. He obtained a PhD in computer architecture from the Microprocessor Research and Development Center at Peking University.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605837856?pwd=kYqJs4bVBt4E0cMCWR6GXH3wxzOoiw.1

Meeting ID: 160 583 7856
Passcode: 161580

Zoom Link: https://github.com/giorgianb/spdhackspring2021/blob/main/bit.ly/spdhack2021

ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
 
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md
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

We will demonstrate how to make a personalized AI partner that can do certain tasks for you. This step by step process can be used to develop your own companion.

Understand what is a Gemini Gems and how it can be personalized for your custom needs. We will show you how to capture the tasks that you would like to complete and give you higher quality responses that will ensure your communication and tasks are completed with ease.

Register here.

Stony Brook Radiology Department Invites you to the AI Research Seminar


Abstract: This talk traces the evolution of AI in neuroradiology through three connected stages: Precision Care, General Medical Intelligence, and Discovery. It moves from multimodal deep learning for stroke outcome prediction, to large-scale foundation models and agent-based medical AI, to imaging biomarkers such as brain aging derived from routine scans. It closes with early-stage work on Galen, a framework for reproducible, scalable, and transparent biomedical imaging AI development and analysis.

Speaker: Yongkai Liu, PhD, Instructor, Department of Radiology (Neuroimaging & Neurointervention), Stanford University

Join Microsoft Teams: https://teams.microsoft.com/meet/221225483255320?p=Anban9lwgOgAq3jaHj
Meeting ID: 221 225 483 255 320
Passcode: kJ7fF3ou
Abstract: In this talk, we take a step back and argue that many varied and seemingly unrelated attacks on the web are actually symptoms of one deeper problem that has existed since the web's inception. Whether it is attacks due to expired domain names, cloaking done by malicious websites, malvertising, or even our growing distrust of the news can be largely attributed back to the issue of stateless linking. Stateless linking refers to the absence of any integrity guarantees between the time that a link for a remote resource was created, to a future time when this link is resolved by web clients. We draw on 10+ years of research to demonstrate how stateless linking and the resulting lack of content integrity is the true culprit for many of our past, current, and likely future web problems. Successfully tackling this one really challenging problem, has the potential of solving many of our web woes.

Bio: Nick Nikiforakis is affiliated with the National Security Institute. He received his PhD in Computer Science from KU Leuven in Belgium. He received his MSc, in Parallel and Distributed Systems and BSc in Computer Science from the University of Crete, Greece. His research focuses on web security and privacy, software security, and intrusion detection.

Location: NCS 120

Chat with Sociology faculty as they share their paths to StonyBrook-what inspired their careers, what led them to teaching,and the experiences that shaped their academic journey.

Dr. Yongjun Zhang

Assistant Professor of Sociology, Departments of Sociology and AAAS

Join this opportunity to talk to Yongjun Zhang about his new interest in the following responsible usage of AI in addressing climate and health issues. Lunch will be served.

Location: SBS Level 4- Sociology Reading Room

View more event information

Come learn of the exciting research being done across so many fields using AI! The recipients of AI3's seed awards will present their work in our showcase on November 17, 2025 and we would love to see you there!

The schedule is listed below.

Location: New Computer Science Room 120

Session 1 - 10:30 AM to 11:45

Kevin Reed, PI, Introducing the AI Techniques in Assessing the Future Changes of Extreme Precipitation and Associated Flood Risks
Co-PIs: Tangnyu Song, Ishrat Dollan
Consultant: Jayesh Rathi

Ruwen Qin, PI, AI-Assisted Analysis of Materials in Recycling Streams
Consultant: Vismay Vora

Giuseppe Gazzola, PI, Using AI to Investigate National Literatures: Italy, France, Spain 1733- 1794
Consultant: Jayesh Rathi

Joseph Lemelin, PI, IAE2^3: AI Ecologies
Co-PIs: Katherine Johnston, Aruna Balasubramanian, Matthew Salzano

Niranjan Balasubramanian, Co-PI, Molecular Foundations for Sustainability: Data Analytics for Sustainable Cellulose Scaffolding Modifications to Remediate Diverse Water Contamination Challenges
PI: Benjamin Hsiao, Co-PI: I. V. Ramakrishnan

Owen Rambow, PI,Achieving Common Ground Through Language and Vision in Mixed-Initiative Human-Machine Communication Via zoom
Co-PI Susan Brennan

Session 2 - 12:30 PM to 1:45

Jack McSweeney, PI, Developing Machine Learning Approaches to Classify Internal Waves
Consultant: Vismay Vora

Eric Josephs, PI, Learning Design Rules to Personalize Precision CRISPR Gene Therapies with Interpretable AI
Consultant: Deboparna Banerjee

Shyam Sharma, PI, Fostering Writing-to-Learn Skills through Critical AI Literacy: A Faculty Development and Student Support Program
Co-PIs: Rose Tirotta-Esposito, Christine Fena

Ritwik Banerjee, PI, A Pragmatic Approach to AI for Digital Media Integrity: Combating Complex Misinformation Through Fallacies and Propaganda
Co-PI: Ruobing Li

Ziyu Shu, Co-PI, Novel Clinical Applications of Deep Image Prior-based CT Image Reconstruction
PI: Xin Qian, Co-PIs: Tiezhi Zhang, Zhaozheng Yin

Prateek Prasanna, Co-PI, An Artificial Intelligence-Driven Clinical Decision Support Tool for the Management of Abdominal Aortic Aneurysm
PI: Apostolos Tassiopoulos, Co-PI's: Mary Saltz, Janos Hajagos, Tahsin Kurc



Presenters will give a 5-minute talk with 2 minutes for Q & A.