The Center of Excellence in Wireless and Information Technology (CEWIT) will host the 16th International Conference on Emerging Technologies for a Smarter World (CEWIT2020) virtually on November 5, 2020. The conference will center on the four major fields which are penetrating our business and personal lives: Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For more info visit: https://www.cewit.org/.
Please join University Libraries on March 29 at 1:00 via Zoom as we welcome Dr. Zhang, SUNY Empire Innovation Professor at SBU's Power Lab. This lab is pioneering the research of coordinated networked microgrids (NMs) that can possibly help to restore neighboring distribution grids after a major blackout. That these NMs hold promise to significantly enhance the day-to-day reliability of the power grids, we are proud to host Dr. Zhang as a member of our STEM Speaker Series. Registration required.
https://library.stonybrook.
Abstract: Trustworthy AI deployment in high-stakes domains requires systems that are fair, private, robust, and controllable as they scale. Yet these demands are often pursued through ad-hoc approaches, lacking a systematic understanding of the inherent trade-offs between competing objectives. We add fairness regularizers and hope bias decreases. We train on massive datasets and hope the model learns the underlying logic of how concepts combine, rather than memorizing statistical shortcuts. We encrypt data and hope the resulting computational overhead remains manageable. But hope isnot a science.
In this talk, I argue that what trustworthy AI lacks is not better heuristics but a deeper science of what these properties fundamentally cost and what is achievable. Before we can fix a system, we must map the terrain: what trade-offs are unavoidable, what regions of performance areunreachable, and how far current methods fall from what is actually achievable. My research builds this map across fairness, privacy, robustness, and controllability, following a common methodology: diagnose where models fail, characterize the fundamental limits any method must obey, and design systems that approach those limits. I will present this framework, its extension to scientific applications where we replace statistical constraints with physical laws to ensure AI systems remain grounded in reality, and a vision for scaling these principles to the rapidly expanding ecosystem of composed and interacting AI systems.
Bio: Dr. Vishnu Boddeti is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University, where he leads the Human Analysis Lab (HAL). His research develops mathematical frameworks for trustworthy AI, spanning fairness, privacy, robustness, and physics-informed learning, with an emphasis on characterizing fundamental limits and building systems that achieve them. His work has been supported by NSF, NIST, DARPA, ONR, Ford, and others, and recognized with a Meta Research Award (2021). His research has been featured on the cover of Nature, recognized as an Editor's Highlight in Nature Communications, and received multiple best paper awards, including the 2024 IEEE-CCF Cloud Computing Best Paper Award and the TMLR Outstanding Certification Finalist (2023). He serves as Senior Area Editor for IEEE Transactions on Information Forensics and Security and completed his PhD in ECE from Carnegie Mellon University in 2012.
Location: NCS 120
The North East AI Agents Day Organizing Committee invites you to '2026 AI Agents Day.'
The goal of this workshop is to offer a comprehensive overview of AI agents, bring ML, Systems, and HCI research communities together to share progress, discuss common problems and evaluation setups, and identify opportunities for collaboration. We aim to bring together attendees from diverse disciplines to foster interdisciplinary collaboration and discuss open research questions.
Location: Jane Street Offices, New York
Register here.
The goal of this workshop is to offer a comprehensive overview of AI agents, bring ML, Systems, and HCI research communities together to share progress, discuss common problems and evaluation setups, and identify opportunities for collaboration. We aim to bring together attendees from diverse disciplines to foster interdisciplinary collaboration and discuss open research questions.
Location: Jane Street Offices, New York
Register here.
Abstract: Formalization of mathematics is the process by which pen-and-paper mathematics is translated into a strict chain of logical deductions down to the axioms of mathematics. The subject has seen renewed interest in the last decades thanks to the development of computer systems called proof assistants, which make this feasible in practice.
There have now been several examples of high-profile mathematical results which have been formalized. In principle, any mathematical domain is accessible. However, existing projects are skewed towards algebra instead of analysis. Notable exceptions are a project which formalized enough of Gromov's convex integration theory to deduce Smale's sphere eversion theorem and the ongoing project to formalize Carleson's convergence theorem for Fourier series.
This workshop will bring together formalization experts and interested mathematicians to give a new impulse to formalization of analysis (in a very broad sense), and to develop abstractions and tools to deduplicate effort.
Application Information: ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate. Some funding may be available for travel and lodging. Graduate students who apply must have their advisor submit a statement of support in order to be considered.
There have now been several examples of high-profile mathematical results which have been formalized. In principle, any mathematical domain is accessible. However, existing projects are skewed towards algebra instead of analysis. Notable exceptions are a project which formalized enough of Gromov's convex integration theory to deduce Smale's sphere eversion theorem and the ongoing project to formalize Carleson's convergence theorem for Fourier series.
This workshop will bring together formalization experts and interested mathematicians to give a new impulse to formalization of analysis (in a very broad sense), and to develop abstractions and tools to deduplicate effort.
Application Information: ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate. Some funding may be available for travel and lodging. Graduate students who apply must have their advisor submit a statement of support in order to be considered.
The deadline to apply for this workshop is January 24, 2026.
Abstract: Robot control has evolved from optimization-based controllers---precise but task-specific---through deep reinforcement learning's learned policies, to Vision-Language-Action (VLA) models that leverage pretrained vision-language backbones for language-conditioned manipulation across diverse tasks.
Despite their promise, VLAs exhibit a critical limitation: they function primarily as trajectory learners rather than skill learners. Recent evaluations reveal that VLAs often fail when faced with even minor variations in object initialization or environmental conditions, suggesting they memorize specific trajectories rather than acquiring generalizable manipulation skills. Attempts to address this through 3D spatial representations have shown limited success, indicating that the missing component may be more fundamental than geometric understanding alone.
This work argues that World Models (WMs)---internal representations that predict future states given actions---constitute the missing piece for robust VLA systems. We present one completed contribution and two ongoing investigations.
We developed a dual-layer world model for human-robot interaction that anticipates both physical scene evolution and latent human preferences for assistive tasks. Building on these foundations, we present ongoing work probing VLA internal representations to verify implicit world model existence, and propose a WM-VLA integration approach operating in the native visual domain through embedding prediction and image decoding.
Together, these contributions and investigations establish a foundation for WM-VLA systems, pointing toward robust, generalizable robot policies.
Speaker: Jason Qin
Location: NCS 220
Despite their promise, VLAs exhibit a critical limitation: they function primarily as trajectory learners rather than skill learners. Recent evaluations reveal that VLAs often fail when faced with even minor variations in object initialization or environmental conditions, suggesting they memorize specific trajectories rather than acquiring generalizable manipulation skills. Attempts to address this through 3D spatial representations have shown limited success, indicating that the missing component may be more fundamental than geometric understanding alone.
This work argues that World Models (WMs)---internal representations that predict future states given actions---constitute the missing piece for robust VLA systems. We present one completed contribution and two ongoing investigations.
We developed a dual-layer world model for human-robot interaction that anticipates both physical scene evolution and latent human preferences for assistive tasks. Building on these foundations, we present ongoing work probing VLA internal representations to verify implicit world model existence, and propose a WM-VLA integration approach operating in the native visual domain through embedding prediction and image decoding.
Together, these contributions and investigations establish a foundation for WM-VLA systems, pointing toward robust, generalizable robot policies.
Speaker: Jason Qin
Location: NCS 220
This session brings together the scientists, agencies, and community partners generating environmental data across New York City to confront a shared challenge: critical atmospheric and marine data is being collected across the region, but too often in silos that limit its reach and impact.
Using Governors Island's environmental sensing efforts as a working case, the program opens into a broader conversation. We will discuss how disparate data streams and objectives across NYC can be coordinated, shared, and activated for the benefit of the broader NYC community. And how that data can support healthier and safer communities, emergency preparedness and resilience, more informed city planning and operations, and better decision-making by businesses and investors.
This session will include a fireside chat on the state of hyperlocal data in NYC with Assistant Commissioner Carolyn Olson, a panel and discussion on deploying hyperlocal data, and a tour of the Governors Island Environmental Observatory (GIEO). We hope to see you there.
Location: 110 Andes Rd New York, NY
Register to join.
Abstract: Facial emotion understanding aims to recognize, represent, and interpret human affect from facial behavior, and it is important for affective computing, human-computer interaction, digital humans, and mental health assessment. Existing work has represented facial emotion through discrete emotion categories, continuous affective dimensions such as valence and arousal, facial landmarks or geometry, and more recently semantic or language-based emotion descriptors. With the development of deep learning, supervised facial expression recognition has achieved strong performance on benchmark datasets, while self-supervised learning, multimodal large language models, and controllable facial generation have introduced new ways to learn emotion-related facial representations from images, videos, text, audio, and speech. However, many current models still rely heavily on manually annotated emotion labels, third-party perception judgments, or multimodal contextual cues, making it difficult to determine how much emotional information is captured directly from facial behavior itself, especially in naturalistic and clinically meaningful settings. Because these limitations make it challenging to evaluate whether facial representations capture emotionally meaningful behavior in real-world interactions, we propose to study facial emotion understanding through the relationship between facial behavior and language-derived emotional expression in psychiatric interview videos. Using a large dataset of mental health interviews, we extract multiple types of facial representations, including Action Unit features from FaceReader and OpenFace, non-AU facial behavior features from OpenFace, and 3D facial representations from EMOCA and SMIRK. We train segment-aligned transformer regressors to predict language-derived emotional targets, including valence, arousal, and RoBERTa-derived semantic-affective features from transcript segments. The results show that all facial representations achieve meaningful predictive performance across MSE and Pearson correlation metrics in both within-participant and between-participant evaluation settings. This indicates that facial behavior encodes information related to linguistic emotion at multiple levels: moment-to-moment emotional variation within individuals and broader affective differences across individuals. These findings suggest that structured facial representations can support vision-based emotion understanding in naturalistic mental health interviews and motivate future work on self-supervised, personalized, and controllable facial emotion models.
Speaker: Shao-Yu Chang
Zoom: https://stonybrook.zoom.us/j/
Speaker: Shao-Yu Chang
Zoom: https://stonybrook.zoom.us/j/
Date of Event
Joel H. Saltz, MD, PhD
SUNY Distinguished Professor Cherith Professor and Founding Chair
Department of Biomedical Informatics
Stony Brook University
Apostolos K. Tassiopoulos, MD, FACS
Professor of surgery and vice chair for quality and outcomes Chief of the Division of Vascular and Endovascular Surgery
Director of the Stony Brook Vascular Center Stony Brook Medicine
Title: Clinical applications of artificial intelligence to improve diagnosis and risk stratification for patients with aortic aneurysms
Time: Wednesday, Feb 17, 2021 3 pm - 4 pm
Join Zoom Meeting
https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISj...
Meeting ID: 956 1719 7636 Passcode: 924293
Learn how to unlock the power of Image and visuals that will enhance your work by asking the experts questions in-person
No registration required - just stop by!
Location: Frank Melville Jr. Memorial Library Galleria (across from the Central Reading room)