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.
Abstract: Large language models are prone to memorizing some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users. There is hope that model alignment---a standard training process that tunes a model to harmlessly follow user instructions---would mitigate the risk of extraction. However, we develop two novel attacks that undo a language model's alignment and recover thousands of training examples from popular proprietary aligned models such as OpenAI's ChatGPT. Our work highlights the limitations of existing safeguards to prevent training data leakage in production language models.

Speaker: Pegah Alipoormolabashi

Location: CS2311
The coach who led Team USA to four Math Olympiad gold medals shares his blueprint for staying irreplaceable in an AI-driven world.

As artificial intelligence transforms our world, what skills will remain uniquely human? How can we prepare for careers in an automated future?

Join Carnegie Mellon mathematics professor Po-Shen Loh for insights on navigating the AI revolution by embracing our humanity.

Dr. Loh brings a distinctive perspective shaped by his dual expertise: serving as national coach of the USA Mathematical Olympiad team (which has won four gold medals under his leadership) and developing innovative solutions for real-world challenges from pandemic response to educational technology.

Through his nationwide speaking tour that reached 250 audiences across 100 cities, he has refined a practical framework for thriving alongside AI.

In this presentation, Dr. Loh will explore how creative problem-solving, judgment, and communication become more valuable as automation grows -- and how students and professionals can build those strengths now.

The session includes real-world examples, guidance for education and careers, and a Q&A.

Speaker: Po-Shen Loh is a social entrepreneur and inventor, working across the spectrum of mathematics, education, and healthcare.

A math professor at Carnegie Mellon University, he also served a decade-long term as the national coach of the USA International Mathematical Olympiad (IMO) team, taking the team to gold on numerous occasions.

He has pioneered numerous innovations and has been featured in or co-created YouTube videos with more than 25 million views.

Location: Wang Center Theater

The series is offered by Stony Brook University's Institute for Creative Problem Solving in collaboration with the National Museum of Mathematics (MoMath) and Brookhaven National Laboratory.

The event is free but space is limited. Please register to reserve your space.

Abstract: As we enter the AI era, domain scientists face a critical question: What can we do to harness AI effectively for scientific discovery? AI has demonstrated remarkable capabilities, from accelerating simulations to uncovering hidden patterns in complex datasets. While these advancements offer unprecedented opportunities, they also raise concerns--AI models often function as black boxes, making it difficult to connect their outputs to established scientific principles. This lack of interpretability can undermine trust and limit adoption, particularly in fields like meteorology where physical understanding is critical.
In this talk, I will explore how interpretable AI can bridge this gap, highlighting its potential to generate explicit, physically meaningful equations rather than opaque neural networks. Through four case studies from my lab, I will showcase how interpretable AI can enhance scientific understanding:
  1. Satellite Precipitation Retrieval: Using AI-based approaches to interpret precipitation retrieval algorithms from AMSU data, we identified critical microwave channels (89 and 150 GHz) that directly link to physical processes in the atmosphere.
  2. Quantitative Precipitation Estimation (QPE): By applying symbolic regression models to polarimetric radar data, we derived mathematical expressions that outperform traditional Z-R relationships and existing QPE algorithms, offering new insights into rainfall microphysics.
  3. Tornado Probability Prediction: Leveraging reinforcement learning-based symbolic deep learning models, we developed interpretable equations that outperform the traditional Significant Tornado Parameter (STP) index, providing a clearer understanding of the relationships between key atmospheric variables and tornado risk.
  4. Domain-Aware Symbolic Regression for Scientific Equations: In our latest work, we introduced a symbolic regression framework that incorporates domain-specific symbol priors extracted from thousands of scientific publications. By encoding common mathematical structures--such as the prevalence of trigonometric functions in physics or logarithmic forms in biology--into a tree-structured reinforcement learning model, we improved both the accuracy and interpretability of discovered equations. This approach accelerates convergence, enforces physical plausibility, and reveals new governing relationships in climate and geophysical data.
Through these examples, I hope to spark discussion on the evolving role of domain scientists in the AI era and inspire new ways to integrate AI with physical understanding in atmospheric research.

IACS Seminar Speaker: Yixin Wen, University of Florida

Location: IACS Seminar Room or Zoom

Join Zoom Meeting: https://stonybrook.zoom.us/j/97596399106?pwd=0PBvElFLqov3biO6OlQxSWLWudkIuH.1
Meeting ID: 975 9639 9106
Passcode: 096213

Abstract: Generative visual models like Stable Diffusion and Sora generate photorealistic images and videos that are nearly indistinguishable from real ones to a naive observer. However, their grasp of the physical world remains an open question: Do they understand 3D geometry, light, and object interactions, or are they mere pixel parrots of their training data? Through systematic probing, I will demonstrate that these models surprisingly learn fundamental scene properties--intrinsic images such as surface normals, depth, albedo, and shading (à la Barrow & Tenenbaum, 1978)--without explicit supervision, which enables applications like image relighting. But I will also show that this knowledge is insufficient. Careful analysis reveals unexpected failures: inconsistent shadows, multiple vanishing points, and scenes that defy basic physics. All these findings suggest these models excel at local texture synthesis but struggle with global reasoning: a crucial gap between imitation and true understanding. I will then conclude by outlining a path toward generative world models that emulate global and counterfactual reasoning, causality, and physics.

Bio: Anand Bhattad is a Research Assistant Professor at the Toyota Technological Institute at Chicago. He earned his PhD from the University of Illinois Urbana-Champaign in 2024 under the mentorship of David Forsyth. His research interests lie at the intersection of computer vision and computer graphics, with a current focus on understanding the knowledge encoded in generative models. Anand has received Outstanding Reviewer honors at ICCV 2023 and CVPR 2021, and his CVPR 2022 paper was nominated for a Best Paper Award. He actively contributes to the research community by leading workshops at CVPR and ECCV, including Scholars and Big Models: How Can Academics Adapt? (CVPR 2023), CV 20/20: A Retrospective Vision (CVPR 2024), Knowledge in Generative Models (ECCV 2024), and How to Stand Out in the Crowd? (CVPR 2025). For more details, visit https://anandbhattad.github.io/


Discover how U.S. Census Bureau Tools can help you find free data for your research projects, community, and more. See how to access the latest American Community Survey and 2020 Census data for various geographies including New York City and Long Island at data.census.gov. Learn about Community Resilience Estimates and how to navigate My Community Explorer; an interactive map-based tool which highlights demographic and socioeconomic data that measure inequality. This session will involve live demonstrations and hands-on exercises for participants. Registrants will receive the Zoom link one day prior to the event.

Please Register for SBU Libraries' AI Club: Exploring Census Data here.

Speaker: Gary Kazantsev (Head of Quant Technology Strategy in the Office of the CTO at Bloomberg)

 

Date/Time: Friday, October 15, 2021 10:00AM-11:00AM EST

 

Title: Machine Learning in Finance

Abstract: Machine learning is changing our world at an accelerating pace. In this talk we will discuss the recent developments in how machine learning and artificial intelligence are changing finance, from a perspective of a technology company which is a key  participant in the financial markets. We will give an overview and discuss the evolution of selected flagship Bloomberg ML and AI projects, such as sentiment analysis, question answering, social media analysis, information extraction and prediction of market impact of news stories. We will discuss practical issues in delivering production machine learning solutions to problems of finance, highlighting issues such as interpretability, privacy and nonstationarity. We will also discuss current research directions in machine learning for finance. We will conclude with a Q&A session.

Bio: (https://www.techatbloomberg.com/people/gary-kazantsev/) Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company's Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.

Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.

He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.


Join Zoom Meetinghttps://stonybrook.zoom.us/j/93374426887?pwd=cE9zeW51VXFEN2R0YnNPbHF1WFp0Zz09Meeting ID: 933 7442 6887Passcode: 330347One tap mobile+16468769923,,93374426887# US (New York)+13126266799,,93374426887# US (Chicago)Dial by your location +1 646 876 9923 US (New York) +1 312 626 6799 US (Chicago) +1 301 715 8592 US (Washington DC) +1 346 248 7799 US (Houston) +1 408 638 0968 US (San Jose) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma)Meeting ID: 933 7442 6887

CSE 600 Seminar Series | Fall 2025


Abstract: Vision-language models that see and describe the world are now part of our daily lives, from internet search and accessibility tools to content generation and automatic moderation. However, as these models grow and become more widely used, their limitations have also become increasingly visible. In particular, it has been shown that these models are unable to reliably perform complex tasks that require abstraction and compositional reasoning. For example, they struggle to decompose an image or text into entities, attributes, and relations, and then reason over new combinations of these elements. As a result, we see generated content full of hallucinations, privacy leaks in images, and different types of biases in the model outputs.In this talk, I will outline a research agenda that aims to build trustworthy vision-language models in the age of generative AI. I will begin with compositional reasoning: how natural language inference can be used to decompose complex instructions and captions into atomic, verifiable statements, improving both evaluation and model behavior on tasks that require multi-step reasoning. I will then discuss how synthetic data and simulated environments can be used to train more reliable models, and how they can also stress-test models beyond standard benchmarks, revealing when models drop attributes, break object relations, or fail under distribution shifts. I will also share recent work on using hallucination correction as a signal to improve video-language alignment, and on privacy-preserving image understanding for blind and low-vision users. I will conclude with possible ways we can systematically probe, debug, and repair these models, turning synthetic perception into something we can trust in real-world deployments.



Speaker: Paola Cascante-Bonilla is a tenure-track Assistant Professor in the Department of Computer Science at Stony Brook University (SUNY). Before that, she was a Postdoctoral Associate at the University of Maryland Institute for Advanced Computer Studies (UMIACS), developing methods and metrics related to trustworthy machine learning. She received her Ph.D. in Computer Science at Rice University in 2024, working on Computer Vision, Natural Language Processing, and Machine Learning.Her research focuses on developing systems that enable compositional reasoning and common-sense inference through vision and language, while tackling issues such as cultural biases, data distribution, explainability, and trustworthy AI. Additionally, Cascante-Bonilla creates simulated environments for embodied agents to learn in a safe, controlled setting, aiming to facilitate effective collaboration and problem-solving for complex tasks by leveraging the implicit knowledge of large-scale pre-trained deep learning models.
Cascante-Bonilla is the recipient of the Ken Kennedy Institute SLB Graduate Fellowship (2022/23), she was selected as a Future Faculty Fellow by Rice's George R. Brown School of Engineering (2023) and as a Rising Star in EECS (2023).
Location: NCS 120