AI Innovation Institute

University Libraries Presents:
Join librarian Christine Fena for an interactive workshop that invites you to explore AI tools first hand, 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.

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Location: Melville Library, Central Reading Room, Lab B

Over the past decade, Artificial Intelligence (AI) has made stunning advances, from mastering language to solving the structure of proteins. These breakthroughs arise from more than forty years of work in neural networks, where ideas from neuroscience have inspired solutions in AI. In this lecture, Anthony Zador, MD, PhD, will explore how reverse engineering the brain's computations has driven progress in both fields, and how this back-and-forth between neuroscience and AI is set to grow even stronger -- with brain-inspired designs driving new AI advances while AI tools transform our understanding of how the brain works.

Speaker:
Dr. Zador works at the intersection of neuroscience and artificial intelligence. He is the Alle Davis Harris Professor of Biology at Cold Spring Harbor Laboratory, where he served as Chair of Neuroscience. He was named one of Foreign Policy's 100 Leading Global Thinkers and is a recipient of the Brain Research Foundation Fellowship, the Gill Symposium Transformative Investigator Award, and the Allen Distinguished Investigator Award.

Watch online at stonybrook.edu/live

Abstract: Generative models have transformed natural image synthesis, driven by billion-scale datasets of image-caption pairs. Specialized domains, such as digital histopathology and satellite imagery, differ in two ways: their images reach gigapixel resolution, far beyond any feasible training resolution, and they lack paired captions, since captioning the patches of a single whole-slide image (WSI) takes about 40 hours of pathologist time. Overcoming these challenges would enable generative models that create teaching cases of rare diseases, simulate tumor progression, and serve as a foundation for digital twins.

In this thesis, we build generative foundation models for these domains, scaling from small patches to gigapixel images without patch-level annotations. We begin with PathLDM, which conditions histopathology patch generation on
slide-level pathology reports, enabling the first text-to-image generation in histopathology. Since reports do not describe individual patches, we propose LRDM, which uses embeddings from pretrained self-supervised learning (SSL) encoders as patch-level conditioning for both histopathology and satellite images. ZoomLDM extends SSL conditioning across eight magnifications with a single model, capturing structure from individual cells to tissue architecture. To scale across cancer types, we present PixCell, the first generative foundation model for histopathology, trained on 30.8 million patches from 69,184 WSIs across 28 organ types. Synthetic images from PixCell can stand in for real data in SSL training, enable privacy-preserving data sharing, and improve downstream tile-level classifiers. Extending this to gigapixel scale, we introduce BigCell, which generates coherent WSIs of up to 32768x32768 pixels by guiding a 2048x2048 flow-matching transformer with a grid of foundation model embeddings, and speeds up generation by over 3x with an importance-based schedule. BigCell enables text-to-image synthesis at 8192x8192 resolution, with synthetic samples improving slide-level few-shot classification by up to 18%. Beyond pathology, our SSL-conditioned models serve as data generators for self-supervised learning on natural images. Finally, to make gigapixel synthesis practical, we plan to reduce BigCell's generation time from about 5 hours to under 20 minutes per 32768x32768 image through few-step distillation and efficient attention, and to extend text-to-image synthesis from 8192x8192 regions to entire slides with a pretrained language model.

Speaker: Srikar Yellapragada

Location: NCS 220

Abstract: Foundation models are rapidly becoming capable assistants for knowledge work, but their deployment in real settings is limited by three gaps: they do not transfer reliably across environments, their internal reasoning is opaque, and their behavior is hard to control precisely. In this talk, I argue that these limitations are not only about model size -- they are fundamentally about whether learning captures and leverages the underlying structure of the data-generating process. I use causal thinking as a practical lens to model what is invariant, what changes, and what can be intervened on, and I further show how this leads to learning principles that improve trustworthiness.
I will first present methods for learning unifying mechanisms from heterogeneous data, across domains and modalities, to enable reliable transfer and controllable generation. Next, I will show how structured concepts can be recovered even from seemingly unstructured data, by analyzing and improving self-supervised objectives (such as masking and diffusion) through hierarchical latent-variable models. These concept structures can then be used to interpret generative models and support targeted, multi-level edits. Finally, I connect these two threads to generalization beyond the training distribution. I will discuss natural conditions for extrapolation and a compositional generation framework that improves prompt following for novel concept combinations. I will conclude with a brief outlook on self-improving world models and AI-assisted scientific discovery.


Bio: Lingjing Kong is a Ph.D. candidate in the Computer Science Department at Carnegie Mellon University. His research focuses on Causal AI for transferable, interpretable, and controllable systems, with an emphasis on understanding and exploiting the structure of real-world data to make foundation models actionable and more reliable. He develops identification principles and scalable algorithms for learning unified models from heterogeneous data, uncovering hierarchical concept structures in unstructured data (e.g., images and text), and generalizing beyond training support through compositionality and extrapolation. His work has appeared in top ML venues including ICML, NeurIPS, CVPR, ICLR, and EMNLP and has been prototyped and applied in industry.

Location: NCS 120

First Day of Classes 2019 Fall Semester

Department of Writing and Rhetoric Lecture

Escaping Human Judgment? The Politics of AI under Authoritarianism

Much of the recent debate around AI-decision making assumes that human involvement is a safeguard: humans bring context, accountability, and fairness to otherwise impersonal algorithmic systems. But whether human or algorithmic judgment is preferred depends on the complex interaction between an individual's social position and the configuration of power in which that judgment takes place.

Speaker: Zheng Fu, PRODIG+ Fellow

Location: Humanities 1008

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 PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.