Speaker TBD
Refreshments will be provided
Join Klaus Mueller, professor of computer science and interim chair of the Department of Technology and Society, as he hosts Sucheta Lahiri.
Lahiri leads the AI Ethics and Risk Management function at Oxy, where she is responsible for ensuring that the company's AI solutions are developed and deployed in a manner that is ethical, efficient, trustworthy, safe, sustainable, and human-centered. She holds a doctorate from Syracuse University, along with two master's degrees in Applied Statistics and Information Science earned in India.
Zoom: https://stonybrook.zoom.us/j/7851507944?omn=98268154363#success
The AI Community at Stony Brook University is proud to announce Datathon 2026.
Dive into data analysis and AI/ML, and get ready to build something big. In this year's underwater-themed event, enjoy a weekend of data analysis, hacking, networking, fun activities, and minigames.
Whether you're a seasoned developer, data scientist, designer, or completely new to hacking, this event is your chance to collaborate, learn data science, and create something impactful with data and AI/ML.
What is Datathon?
AI Community's Datathon is the premier data science competition at Stony Brook University, bringing together students of all skill levels for a weekend of data exploration, analysis, and innovation. Just like a typical hackathon, you will be using your skills to build your dream project.
Unlike a regular hackathon, Datathon is focused on data science. You will be given a set of data to work with, analyze, and apply to your project. You can also find your own data to use. Your project will be presented to a panel of judges consisting of professors and industry professionals!
Who Can Participate
- Students of all skill levels and majors are welcome.
- Come with a team or find one at the event or on Discord.
- This event is open to SBU and non-SBU students.
* Non-SBU Undergraduate Students are ineligible to receive prizes
* You must be 18+ or older (Excludes minors who are active SBU students)
Location: SAC Ballroom B
Register here.
How to Do Spectral Learning at Scale for Science and Engineering
Abstract: Spectral decompositions such as singular value decompositions (SVDs) and eigenvalue decompositions (EVDs) are central tools across a vast swath of scientific computing and machine learning, with abundant engineering applications. Yet many modern methods for learning such decompositions in high dimensions struggle with instability, bias, and poor scalability, even when approximation power is not the limiting factor. I argue that these difficulties are not intrinsic to spectral problems, but instead arise from a shared reliance on Rayleigh-quotient-based constrained optimization, which forces explicit orthogonality handling through penalties, normalization, or whitening.
To address these challenges, I present a reformulation based on unconstrained variational objectives that implicitly encode spectral structure, eliminating the need for orthogonalization and ad-hoc regularization. This perspective leads to a conceptually simpler and scalable parametric framework for learning ordered spectral representations via nested optimization. The resulting framework is well matched to diverse settings in science and engineering. As examples, I demonstrate its effectiveness on eigenvalue problems for linear PDEs such as the Schrödinger equation, spectral (Koopman) analysis of nonlinear dynamical systems such as molecular dynamics, and structured representation learning with deep neural nets. Collectively, these examples illustrate how abandoning Rayleigh-quotient-based formulations resolves long-standing optimization pathologies across domains.
Bio: Jongha (Jon) Ryu is a postdoctoral associate at MIT EECS. He received his Ph.D. in Electrical and Computer Engineering from UC San Diego. His research develops statistical and mathematical foundations for scientific machine learning, with a focus on scalable spectral methods, efficient generative modeling, and reliable uncertainty quantification for scientific and engineering systems.
Location: NCS 120
Abstract: Spectral decompositions such as singular value decompositions (SVDs) and eigenvalue decompositions (EVDs) are central tools across a vast swath of scientific computing and machine learning, with abundant engineering applications. Yet many modern methods for learning such decompositions in high dimensions struggle with instability, bias, and poor scalability, even when approximation power is not the limiting factor. I argue that these difficulties are not intrinsic to spectral problems, but instead arise from a shared reliance on Rayleigh-quotient-based constrained optimization, which forces explicit orthogonality handling through penalties, normalization, or whitening.
To address these challenges, I present a reformulation based on unconstrained variational objectives that implicitly encode spectral structure, eliminating the need for orthogonalization and ad-hoc regularization. This perspective leads to a conceptually simpler and scalable parametric framework for learning ordered spectral representations via nested optimization. The resulting framework is well matched to diverse settings in science and engineering. As examples, I demonstrate its effectiveness on eigenvalue problems for linear PDEs such as the Schrödinger equation, spectral (Koopman) analysis of nonlinear dynamical systems such as molecular dynamics, and structured representation learning with deep neural nets. Collectively, these examples illustrate how abandoning Rayleigh-quotient-based formulations resolves long-standing optimization pathologies across domains.
Bio: Jongha (Jon) Ryu is a postdoctoral associate at MIT EECS. He received his Ph.D. in Electrical and Computer Engineering from UC San Diego. His research develops statistical and mathematical foundations for scientific machine learning, with a focus on scalable spectral methods, efficient generative modeling, and reliable uncertainty quantification for scientific and engineering systems.
Location: NCS 120
The next AI Institute seminar speaker will be Chao Chen of Biomedical Informatics, on Monday November 29 at noon via zoom:
https://stonybrook.zoom.us/j/
He will be talking on the Detection of Trojan Attacks to Deep Neural Networks - A Topological Perspective, with his abstract and bio below.
Abstract: Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through Trojaned training samples, i.e., samples with special trigger injected and labels altered. To identify a Trojaned model at deployment is challenging, due to limited access to the training data. We propose to identify Trojaned neural networks using methods from topological data analysis. In particular, we propose to (1) inspect high-order topological features of the neuron interactions and (2) reverse engineer the injected triggers using a topological loss. These approaches take different angles and reveal insights into the behavior of neural networks when their strong memorialization power is exploited maliciously. The work has been accepted to NeurIPS'21. I will also briefly mention other research directions from my group, including incorporating topological information into deep image analysis, topology-inspired graph neural networks, and robust training of neural networks with label noise. These works have been published in ICLR, ICML, NeurIPS, ECCV, ICCV and AAAI in recent years.
Bio: Dr. Chao Chen is an assistant professor of Biomedical Informatics at Stony Brook University. His research interests span topological data analysis (TDA), machine learning and biomedical image analysis. He develops principled learning methods inspired by the theory from TDA, such as persistent homology and discrete Morse theory. These methods address problems in biomedical image analysis, robust machine learning, and graph neural networks from a unique topological view. His research results have been published in major machine learning, computer vision, and medical image analysis conferences. He is serving as an area chair for MICCAI, AAAI, CVPR and NeurIPS.
Biomedical Informatics Grand Rounds Talk
Educational objectives:
Speaker: Dr. Janos Hajagos, Ph.D Chief Data Analytics and Research Assistant Professor, Biomedical Informatics, Stony Brook University
Remote Access: Join Zoom meeting
Meeting ID: 95617197636 Passcode: 924293
Educational objectives:
- Explain how generative AI can write data-analysis code from natural-language prompts
- Describe how virtual computational laboratories can be built to explore clinicalquestions
- Identify the key challenges of applying AI models to protected healthcare data
Speaker: Dr. Janos Hajagos, Ph.D Chief Data Analytics and Research Assistant Professor, Biomedical Informatics, Stony Brook University
Remote Access: Join Zoom meeting
Meeting ID: 95617197636 Passcode: 924293
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:
Location: Humanities 1006
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
DeepMath Conference on the Mathematical Theory of Deep Neural Networks
Recent advances in deep neural networks (DNNs), combined with open, easily-accessible implementations, have made DNNs a powerful, versatile method used widely in both machine learning and neuroscience. These advances in practical results, however, have far outpaced a formal understanding of these networks and their training. The dearth of rigorous analysis for these techniques limits their usefulness in addressing scientific questions and, more broadly, hinders systematic design of the next generation of networks. Recently, long-past-due theoretical results have begun to emerge from researchers in a number of fields. The purpose of this conference is to give visibility to these results, and those that will follow in their wake, to shed light on the properties of large, adaptive, distributed learning architectures, and to revolutionize our understanding of these systems.
Join a collaborative workshop series designed to help you redesign writing-intensive assignments in the age of AI. The program includes four self-paced modules, weekly interactive workshops (September 18, 25, and October 2, 9), student resources, and a SUNY-wide virtual symposium. Applications are accepted through August 31.
https://you.stonybrook.edu/campusannouncements/2026/06/22/june-22-2026/
https://you.stonybrook.edu/campusannouncements/2026/06/22/june-22-2026/
Artificial Intelligence is rapidly reshaping research, education, and industry--but its growth carries important environmental implications. From the energy demands of large-scale computing to AI's potential to advance climate modeling, conservation, and sustainable design, the relationship between AI and the environment is both challenging and promising. This interdisciplinary panel explores AI's ecological footprint, its role in environmental solutions, and how universities can pursue innovation while upholding sustainability commitments.
Panelists:
Dana Golden -- PhD student in Economics, Stony Brook University.
Dr. Sharon Pochron -- Associate Professor in Sustainability Studies Program, School of Marine and Atmospheric Sciences, Stony Brook University.
Dr. Jordanna Sprayberry -- Associate Professor, Ecology & Evolution, Director of Undergraduate Biology, Stony Brook University.
Dr. Lav Varshney -- Director of the Artificial Intelligence Innovation Institute (AI3) and inaugural Della Pietra Infinity Chair, Stony Brook University.
Register here.
Panelists:
Dana Golden -- PhD student in Economics, Stony Brook University.
Dr. Sharon Pochron -- Associate Professor in Sustainability Studies Program, School of Marine and Atmospheric Sciences, Stony Brook University.
Dr. Jordanna Sprayberry -- Associate Professor, Ecology & Evolution, Director of Undergraduate Biology, Stony Brook University.
Dr. Lav Varshney -- Director of the Artificial Intelligence Innovation Institute (AI3) and inaugural Della Pietra Infinity Chair, Stony Brook University.
Register here.