Abstract: Many foundation models for digital pathology have been released recently. Benchmarking available methods then becomes paramount to get a clearer view of the research landscape. For this reason, we introduce THUNDER, a tile-level benchmark for digital pathology foundation models, allowing for efficient comparison of many models on diverse datasets with a series of downstream tasks, studying their feature spaces and assessing the robustness and uncertainty of predictions informed by their embeddings. Such foundation models are often used as feature extractors and combined with Multiple Instance Learning (MIL) aggregators at downstream time. Such aggregation must be efficient and reliable. We will focus on two specific examples of this: (I) HistAug, a fast and efficient generative model for controllable augmentations in the latent space of foundation models to perform data augmentation for MIL, and (ii) CAR-MIL, a method based on counterfactual attention regularisation to improve the reliability of attention maps of MIL methods.

Short-bio: Pierre Marza is a Postdoctoral Researcher at CentraleSupelec in the Biomathematics team of the MICS lab, studying Computer Vision and Deep Learning for Medical Imaging, with a focus on Digital Pathology. Prior to this, he was a PhD student at INSA Lyon, in the LIRIS and CITI labs, advised by Christian Wolf, and co-advised by Laetita Matignon and Olivier Simonin. He studied Visual Navigation, Embodied AI, Spatial Reasoning, more specifically how to learn to represent 3D space, generalize to new environments and master diverse tasks from light supervision.

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/94798224254?pwd=CFraer25qnpORbJ14aAVHRwaSJOjJM.1
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 to attend. The seminar will be taught by Prof. Chao Chen, chao.chen.1@stonybrook.edu.

The Provost's Office is excited to invite you to join in responding to an extraordinary opportunity to enhance our academic and research capabilities in AI at Stony Brook. SUNY recently made funding available to support the creation of departments of AI and Society at its universities. Stony Brook is well-positioned to seize this opportunity to build upon our interdisciplinary strengths in AI.

The office is hosting a forum on Friday, Nov. 15, from 11:30 a.m. to 1:30 p.m., in Ballroom A, SAC. You are invited to attend to learn more about this opportunity and to help us generate ideas to build a compelling proposal for Stony Brook to submit to SUNY. Lunch will be provided.

Please click here to RSVP as soon as possible.

This funding will support innovation in our curriculum, allowing us to create programs that explore the social and societal impact of AI alongside the technological advancements led by researchers in engineering and scientific disciplines.

We believe we can make a significant impact through this SUNY program and look forward to your participation in this initiative.
Since 2021, IIAC has gathered the heads of nations, fabs, and faculties who shape how intelligence is built into the physical world. After four editions at Stanford and a 2025 convening in Ha Noi, the fifth moves to Tokyo for a single, deliberate day on the question beneath every other question: who governs the intelligence that runs the factory, the grid, and the supply chain?

Keynote speakers: Christopher Nguyen (Aitomatic), Yann LeCun (AMI Labs), Akihisa Shiozaki (House of Representatives, Japan), Suresh Venkatarayalu (Honeywell), Jimmy Rhee (ASI Foundry), and Toshikazu Okuya (Commerce and Information Policy Bureau).

Register here to attend.
Abstract: Artificial Intelligence (AI) is no longer a futuristic concept -- it is here, but its development, benefits, and risks remain unevenly distributed across industries, nations, and social groups. In this talk, Jieshu presents her research on the societal dimensions of AI from two perspectives: the forces shaping AI's development (backward-looking) and its current and potential impact on society (forward-looking). She first examines disparities in AI, including women's underrepresentation in AI patents and the geographic concentration of AI innovation, highlighting inequalities in who creates AI and who benefits from it. She then explores AI's societal impact, focusing on workforce transformation and the need for GenAI literacy. She will also discuss AI patents, AI's role in climate change mitigation and adaptation, potential environmental biases in LLMs, and gender-specific patterns in AI portrayals in science fiction.

Bio: Jieshu Wang is a Postdoctoral Research Scholar at Arizona State University (ASU), focusing on the social dimensions of artificial intelligence (AI). With a background in engineering, economics, communication, and science and technology studies, she examines how AI both shapes and is shaped by broader societal forces. Her research employs interdisciplinary methods to explore the social, political, and economic factors influencing AI development, as well as its role in innovation, the economy, the future of work, climate change mitigation, and popular culture. Jieshu holds a Ph.D. in Human and Social Dimensions of Science and Technology from ASU. She is also a science book translator and has translated six books.

Location: Old Computer Science, room 1310
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: 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/


Professor Petar M. Djuric, SUNY Distinguished Professor and Savitri Devi Bangaru Professor in Artificial Intelligence at Stony Brook University, has been selected as a plenary speaker at the upcoming 23rd IEEE Statistical Signal Processing Workshop (SSP 2025). The event will be held from June 8-11, 2025, in Edinburgh, Scotland, and is one of the premier international forums for the latest advances in statistical signal processing.

Professor Djuric's plenary talk, titled Quantifying causal relationships: Dynamic strengths, attributions, and confounders, will take place on June 10 from 9:00 AM to 10:00 AM EST. His presentation addresses foundational challenges in data-driven causality, proposing novel methodologies for quantifying causal strength in both static and dynamic systems, with special attention to latent confounders and attribution analysis.

This work has broad implications across disciplines including healthcare, economics, and climate science--areas where causal understanding drives critical decisions and innovations.

Professor Djuric has been a long-standing leader in the fields of machine learning and signal and information processing. After receiving his Ph.D. from the University of Rhode Island, he joined the faculty at Stony Brook University, where he served as Chair of the Department of Electrical and Computer Engineering from 2016 to 2023. He is also the founding Editor-in-Chief of the IEEE Transactions on Signal and Information Processing Over Networks and a Fellow of IEEE, EURASIP, AAIA, and AIIA.

Early bird registration for the workshop is open until April 30, 2025. For more information, visit the official SSP 2025 website.