AI Innovation Institute

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.

Face Editing with Machine Learning presented by Zhixin Shu

ABSTRACT: The face is the most informative feature of humans and has been a long-standing research topic in Computer Vision and Graphics. Images of faces are also ubiquitous in photography and social media, and people have devoted significant resources to capturing and editing face images. Face editing can be broadly viewed as the encoding, manipulation and the decoding of some representations for face images. The challenges are that we want to manipulate an image in a controllable way and generate results that are both desirable and as realistic as possible. This thesis explores different Machine Learning-based face-editing approaches. I discuss the role of machine learning for achieving desirable edits by learning both the physical aspects as well as the statistical manifold of human faces. In my work for eye-editing, I discuss the importance of understanding multiple physical elements of a face image, such as shape, illumination, pose, etc. In a deep-learning-based approach, I introduce image formation domain knowledge to the construction and training of a neural network. This network provides transparent access to the disentangled representations of the aforementioned physical properties. With this network, we can achieve various face editing tasks in forms of representation manipulation. After that, I introduce Deforming Autoencoders, a network that learns to disentangle shape and appearance in an unsupervised manner. This disentanglement is beneficial for the learning of some other factors of variations, such as illumination and facial expression. In an extension of Deforming Autoencoders, we incorporate non-rigid structure-from-motion to learn a 3D morphable model for faces that only requires an image set for training. At last, I describe an image-to-image network for 3D face reconstruction, which also utilizes structure-from-motion in deep learning. With real face images in training, this network not only reconstructs 3D faces more accurately than prior art but also has better generalization ability in real-life testing cases.

2026 MIC Symposium on Multilingualism | Featured Talk

Searching for Common Ground in Human-AI Interaction (What Gen AI Can't Do)

Abstract: For many of us, the alien invasion of LLMs has been unsettling, at best. Who knew that monstrous quantities of text and compute power could simulate human discourse processing? But despite claims about emergent theory-of-mind in reasoning models, LLMs remain stochastic parrots: they do not collaborate, project co-presence, form conceptual pacts, accurately signal commitment to what they say, or accrue common ground. This lack of pragmatic competence is clear from our recent studies of narrative and referential communication by AI vs. humans.

Speaker: Susan Brennan is SUNY Distinguished Professor at Stony Brook University in Cognitive Science within the Department of Psychology, with affiliations in Computer Science and Linguistics. Her research uses eye-tracking, experimental, behavioral, and observational methods to study the psycholinguistics of communication (verbal, nonverbal, and multimodal), adaptive language use, and the human use of technology, including spoken dialogue systems. She currently directs the NSF-funded Research Traineeship project, Detecting and Addressing Bias in Data, Humans, and Institutions, a collaboration among PhD. programs in Cognitive Science, Social & Health Psychology, Computer Science, Applied Math & Statistics, Linguistics, Political Science, Economics, Africana Studies, and Women's Studies. This traineeship supports students' AI and bias-related convergent research projects that include assessing potential for LLVMs (Large Language-Vision Models) to act as interlocutors in spontaneous dialogue as well as collaborating with the Innocence Network about AI tools. She holds a Ph.D. in cognitive psychology from Stanford University, an M.S. in visual studies from MIT (from what is now known as the MIT Media Lab), and a B.A. from Cornell University in cultural anthropology. She has worked in industry at Atari Research, Hewlett-Packard Labs, and Apple Computer, as well as at NSF as a program officer for the Graduate Research Funding Program.

Faculty Response Panel:

• Jiwon Hwang (Panel Chair; Assistant Professor, Asian and Asian American Studies)
• Rose Tirotta-Esposito (Director, Center for Excellence in Learning and Teaching)
• Lav Varshney (Director, AI Innovation Institute)
• Josh Zhang (Associate Professor, Sociology and Institute for Advanced Computational Science)

Location: Humanities 1006

Millions, Billions, Zillions: Why (In)numeracy Matters by Brian Kernighan, Princeton University 2019 * 2020 Shutterstock Distinguished Lecture Series

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

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.

Location: Melville Library, Central Reading Room, Lab B

https://library.stonybrook.edu/library-events/critiquing-ai/

Please join us for the next CSE 600 Seminar this Friday, October 11th, at 2:30pm in New Computer Science 120 given by Assistant Professor Mohammad Javad Amiri. Abstract: Today's distributed transaction processing systems must deal with untrustworthy environments where multiple mutually distrustful entities communicate with each other, and maintain data on untrusted infrastructure. Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by distributed transaction processing systems to establish consensus on the order of transactions. However, the proliferation of different BFT protocols has made it difficult to navigate the BFT landscape, let alone determine the protocol that best meets application needs. Moreover, as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof distributed transaction processing systems need to be designed with full-stack adaptivity in mind. This talk presents our vision for a reinforcement learning (RL)-based distributed transaction processing system that adjusts effectively in real-time to changing fault scenarios and workloads.

Abstract: Human gaze behavior is a fundamental cue for understanding social intent, human-machine interaction, and cognitive processes. This thesis addresses the challenges of gaze target estimation (GTE), also known as gaze following, by developing a holistic understanding of gaze in complex environments.

The first part of this work improves GTE performance by introducing Patch-level Distribution Prediction (PDP). Unlike traditional models that rely on strict pixel-wise regression, PDP models gaze as a distribution over patches, which better accounts for annotation variance and bridges the gap between target location and in/out-of-frame prediction. To address the laborious nature of data labeling, the second part presents GCDR, the first semi-supervised method for gaze following. By prompting large Visual Question Answering (VQA) models to generate initial Grad-CAM heatmaps and refining them with a diffusion model, this method achieves high performance with significantly fewer human annotations. The third part expands the applicability of GTE to multi-camera environments. By introducing the Multi-View Gaze Target (MVGT) dataset, along with two novel frameworks for integrating information between multiple views and predicting the gaze target across views, we explore a new direction that overcomes single-view limitations such as face occlusion and out-of-view targets.

Building on these foundations, the final part of this thesis proposes a new direction toward semantic social gaze understanding using next-generation multimodal Large Language Models (LLMs). Rather than focusing solely on geometric gaze target localization, we aim to enrich gaze prediction with semantic and relational interpretation in complex social scenes. To this end, we will leverage existing gaze following datasets to derive social gaze supervision, including mutual gaze and shared attention, and obtain aligned language descriptions of scene-level gaze behaviors. This proposed work will enable the model to not only locate gaze targets but also predict structured social gaze relations among individuals, meanwhile generating a concise natural-language summary describing the dominant gaze interactions. By integrating spatial gaze estimation, social relation reasoning, and language-based scene understanding within a unified multimodal model, this work takes an important step toward a holistic understanding of human gaze behavior in real-world environments.

Speaker: Qiaomu Miao

Abstract: The remarkable success of large foundational models, such as LLMs and diffusion models, is built on their learning over vast amounts of static data from the Internet. However, human learning and problem-solving are fundamentally interactive processes--humans learn by engaging with their environment, tools, search engine, and feedback loops, iteratively refining their understanding and decisions. This gap between the interactivity of human learning and the static nature of model training raises a critical question: how can we imbue foundational models with the capacity for meaningful interaction?

In this talk, I will explore methods to enhance foundational models by incorporating interaction with the external environment. I will discuss strategies such as leveraging external tools, compilers, function calls to provide dynamic feedback to enhance foundation models. By drawing inspiration from human's interactive learning processes, I demonstrate how interaction-driven learning can lead to models that are not only more accurate but also more adaptable to real-world applications.

This work bridges the gap between static training paradigms and the dynamic, iterative nature of human intelligence, paving the way for a new generation of interactive AI systems.

Bio: Wenhu Chen has been an assistant professor at the Computer Science Department in University of Waterloo and Vector Institute since 2022. He obtained the Canada CIFAR AI Chair Award in 2022 and CIFAR Catalyst Award in 2024. He has worked for Google Deepmind as a part-time research scientist since 2021. Before that, he obtained his PhD from the University of California, Santa Barbara under the supervision of William Wang and Xifeng Yan. His research interest lies in natural language processing, deep learning and multimodal learning. He aims to design models to handle complex reasoning scenarios like math problem-solving, structure knowledge grounding, etc. He is also interested in building more powerful multimodal models to bridge different modalities. He received the Area Chair Award in AACL 2023, the Best Paper Honorable Mention in WACV 2021, the Best Paper Finalist in CVPR 2024, and the UCSB CS Outstanding Dissertation Award in 2021.