University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting!
Location: Melville Library, Scholarly Communication Seminar Room
Ada Lovelace's Birthday
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
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:
People shift their visual attention to gather and prioritize information from their surroundings, helping them navigate complex environments. Understanding these attentional shifts involves decoding the features that guide where attention is directed (spatial areas of focus) and when attention shifts (timing). Decoding these processes can aid applications from interface design to medical diagnosis. However, prior models have not fully explored the underlying factors addressing these aspects. In this dissertation, we study the factors that guide visual attention across diverse image types, spanning natural images, graphic design documents, and whole slide images (WSIs) of cancer tissues, while also predicting visual attention based on these factors.
First, we propose a method to quantify object recognition uncertainty as a factor influencing spatio-temporal attention (where and when) in natural images. We found that it plays a larger role than bottom-up saliency in guiding visual attention. Second, we analyze graphic design documents such as webpages, comics, posters, mobile UIs, etc., which differ from natural images in that they are designed to convey specific messages or elicit desired viewer response. We propose a unified and interpretable deep learning model that predicts both static and dynamic visual attention behavior (addressing where and when) by integrating document layout and content saliency as factors, enhancing attention prediction performance. Finally, in the domain of digital pathology, we investigate pathologists' attention during their examination of giga-pixel WSIs of prostate cancer with an objective to aid in the development of computer-assisted pathology training and clinical decision support systems. Using a digital microscope interface, we collected the largest known dataset of pathologist attention, which allows us to study the factors that guide their spatial and temporal attention patterns (where and when) and develop predictive models. Our study explores key factors guiding their attention, including magnification, slide staining, the nature of the diagnostic task, and their expertise. Motivated by this analysis, we propose deep learning models to solve two tasks: 1) predicting pathologist attention via spatial (heatmaps) and spatio-temporal (scanpaths) models, and 2) inferring pathologist expertise level, both essential technical components towards developing an AI-assisted pathology training pipeline.
Speaker:
Souradeep Chakraborty
People shift their visual attention to gather and prioritize information from their surroundings, helping them navigate complex environments. Understanding these attentional shifts involves decoding the features that guide where attention is directed (spatial areas of focus) and when attention shifts (timing). Decoding these processes can aid applications from interface design to medical diagnosis. However, prior models have not fully explored the underlying factors addressing these aspects. In this dissertation, we study the factors that guide visual attention across diverse image types, spanning natural images, graphic design documents, and whole slide images (WSIs) of cancer tissues, while also predicting visual attention based on these factors.
First, we propose a method to quantify object recognition uncertainty as a factor influencing spatio-temporal attention (where and when) in natural images. We found that it plays a larger role than bottom-up saliency in guiding visual attention. Second, we analyze graphic design documents such as webpages, comics, posters, mobile UIs, etc., which differ from natural images in that they are designed to convey specific messages or elicit desired viewer response. We propose a unified and interpretable deep learning model that predicts both static and dynamic visual attention behavior (addressing where and when) by integrating document layout and content saliency as factors, enhancing attention prediction performance. Finally, in the domain of digital pathology, we investigate pathologists' attention during their examination of giga-pixel WSIs of prostate cancer with an objective to aid in the development of computer-assisted pathology training and clinical decision support systems. Using a digital microscope interface, we collected the largest known dataset of pathologist attention, which allows us to study the factors that guide their spatial and temporal attention patterns (where and when) and develop predictive models. Our study explores key factors guiding their attention, including magnification, slide staining, the nature of the diagnostic task, and their expertise. Motivated by this analysis, we propose deep learning models to solve two tasks: 1) predicting pathologist attention via spatial (heatmaps) and spatio-temporal (scanpaths) models, and 2) inferring pathologist expertise level, both essential technical components towards developing an AI-assisted pathology training pipeline.
Speaker:
Souradeep Chakraborty
Location: New Computer Science Bldg., Room 220
Zoom Link: https://stonybrook.zoom.us/j/
Meeting ID: 975 528 8447
Passcode: 338037
How to Succeed in Language Design Without Really Trying presented by Professor Brian Kernighan
ABSTRACT: Why do some languages succeed while others fall by the wayside? I've helped create nearly a dozen languages (mostly small) over the years; a handful are still in widespread use, while others have languished or simply disappeared. I've also been present at the creation of several other languages, including some really major ones. In this talk I'll give my humble, but correct, opinion on factors that affect success and failure, and try to offer some insight into what to do if you're trying to design a new language yourself, and why that might be a good thing.
BIO: Brian Kernighan received a PhD in electrical engineering from Princeton in 1969. He joined the Computer Science department at Princeton in 2000, after many years at Bell Labs. He is a co-creator of several programming languages, including AWK and AMPL, and of a number of tools for document preparation. He is the co-author of a dozen books and some technical papers, and holds 5 patents.
He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences. His research areas include programming languages, tools and interfaces that make computers easier to use, often for non-specialist users. He has also written two books on technology for
non-technical audiences: Understanding the Digital World in 2017 and Millions, Billions, Zillions: Defending Yourself in a World of Too Many Numbers, published in 2018. His most recent book, Unix: A History and a Memoir, was published in October 2019.
ABSTRACT: Why do some languages succeed while others fall by the wayside? I've helped create nearly a dozen languages (mostly small) over the years; a handful are still in widespread use, while others have languished or simply disappeared. I've also been present at the creation of several other languages, including some really major ones. In this talk I'll give my humble, but correct, opinion on factors that affect success and failure, and try to offer some insight into what to do if you're trying to design a new language yourself, and why that might be a good thing.
BIO: Brian Kernighan received a PhD in electrical engineering from Princeton in 1969. He joined the Computer Science department at Princeton in 2000, after many years at Bell Labs. He is a co-creator of several programming languages, including AWK and AMPL, and of a number of tools for document preparation. He is the co-author of a dozen books and some technical papers, and holds 5 patents.
He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences. His research areas include programming languages, tools and interfaces that make computers easier to use, often for non-specialist users. He has also written two books on technology for
non-technical audiences: Understanding the Digital World in 2017 and Millions, Billions, Zillions: Defending Yourself in a World of Too Many Numbers, published in 2018. His most recent book, Unix: A History and a Memoir, was published in October 2019.
Event Website: bnl.gov/nysds
Dates: September 28-29, 2026
Location: SUNY Global Center, New York, NY
Co-hosts: Brookhaven National Laboratory, the Institute for Advanced Computational Science (IACS), and the AI Innovation Institute at Stony Brook University.
Join us for this premier annual conference that brings together researchers and thought leaders from academia, national labs, and industry to exchange ideas and foster cross-disciplinary collaboration centered on data-driven science and technology.
The theme of NYSDS 2026 is Transformational AI from Science to Society. This year's conference will focus on the ways in which artificial intelligence (AI), machine learning (ML) and robotics are impacting everything from our everyday lives to scientific endeavors. NYSDS2026 will feature the following main tracks:
Each track will include invited presentations, contributed talks, posters, and panel discussions.
Dates: September 28-29, 2026
Location: SUNY Global Center, New York, NY
Co-hosts: Brookhaven National Laboratory, the Institute for Advanced Computational Science (IACS), and the AI Innovation Institute at Stony Brook University.
Join us for this premier annual conference that brings together researchers and thought leaders from academia, national labs, and industry to exchange ideas and foster cross-disciplinary collaboration centered on data-driven science and technology.
The theme of NYSDS 2026 is Transformational AI from Science to Society. This year's conference will focus on the ways in which artificial intelligence (AI), machine learning (ML) and robotics are impacting everything from our everyday lives to scientific endeavors. NYSDS2026 will feature the following main tracks:
Robotics and Embodied AI: advances in perception, control, learning and interaction for autonomous physical agents in real-world and scientific environments.
AI for Science: innovative applications ranging from the physical sciences to biology and medicine, and discussion of important ways in which AI is changing the nature of science.
AI for Energy: how energy is discovered and produced, how hazards are mitigated to ensure reliability of the power grid, and our search for new sources of critical minerals and materials.
AI for Risk Assessment: inventive uses of large amounts of data and the AI tools to assess and mitigate risks in national security, climate, and financial markets.
AI for Education: the ways in which education is being reimagined in the age of AI and the roadblocks to training the next generation of students.
Each track will include invited presentations, contributed talks, posters, and panel discussions.
Abstract: As intelligent systems become more integrated into human environments, fostering trustworthy human-AI collaboration presents a pressing challenge. In this talk, I examine the interplay between an agent's performance and social dynamics in shaping trust in human-AI interactions. My approach combines testbed development, behavioral prototyping, and user study design to create controlled experimental setups that capture real-world interaction complexities, such as ambiguity, multi-agent dynamics, and conflicting goals.
I illustrate this with a recent VR study on multi-user interaction with an autonomous vehicle (AV). Moving beyond dyadic interactions, the study probes human perspectives from the roles of a pedestrian, driver, and AV passenger, all interacting with the AV simultaneously at an ambiguous all-way stop sign intersection. We compare interactions with efficient and prosocial AV behavior strategies, revealing diverging trust perceptions and preferences across user roles. These insights inform a broader research trajectory focused on balancing performance with social considerations in designing trustworthy human-AI collaborations.
Bio: JiHyun Jeong is a postdoctoral researcher at Cornell University working on human-computer interaction and human-robot interaction. Her research develops prototypes and methods to explore performance and social factors that influence collaboration and trust between humans and artificial agents. She holds a Ph.D. and MPS in Information Science from Cornell University, and a BSc in Computer Science and Engineering from Korea University. She is a recipient of an honorable mention for best paper at DIS.
Zoom: https://stonybrook.zoom.
Meeting ID: 987 3823 4619
Passcode: 474618
Fall 2026, Wednesdays 2 to 3:20 pm, NCS 220 and Zoom link to be announced soon.
The seminar will be jointly taught by Prof. Dimitris Samaras (samaras@cs.stonybrook.edu).
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!
The seminar will be jointly taught by Prof. Dimitris Samaras (samaras@cs.stonybrook.edu).
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!
Abstract:
Large language models (LLMs) have transformed the way humans write code, bringing unprecedented automation to software development. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with a deeper, more comprehensive understanding of software programs. Finally, I will discuss the future of AI-driven software engineering, envisioning a new era of automation that is more reliable, intelligent, and cost-efficient.
Bio:
Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.
Location:
NCS 120
This event brings together people with interests in Computer Vision theory and techniques and examines current research issues in the field.
Each seminar consists of multiple short talks (around 15 minutes) by several students.
Join Zoom Meeting:
https://stonybrook.zoom.us/j/93547152068?pwd=WVpoRVgzelBXeloxdXVEakNSb2M5UT09
Meeting ID: 935 4715 2068 | Passcode: 481832
Each seminar consists of multiple short talks (around 15 minutes) by several students.
Join Zoom Meeting:
https://stonybrook.zoom.us/j/93547152068?pwd=WVpoRVgzelBXeloxdXVEakNSb2M5UT09
Meeting ID: 935 4715 2068 | Passcode: 481832