The Artificial Intelligence Innovation Institute (AI^3), with administrative support from the Office of the Vice President for Research (OVPR), invites applications to a seed grant program for collaborative projects in artificial intelligence, along three distinct tracks: Collaborative Research in AI, Technical Support for Discipline-Centric Research, and Seed Grants for AI Education and Service.

The program will fund projects for up to a one-year period, depending on the availability of funds. AI^3 anticipates making at least six awards on this call. A one-year, no-cost extension can be requested in the final 6 months of a project with approval subject to progress towards project goals and active participation in research themes.

Competitive applications will actively incorporate modern AI technologies into the work; integrate students; document significant potential for future funding or other growth-oriented outcomes; and highlight innovations.

The 2024 application deadline will be October 15, at 11:59 PM EST. Recipients will be notified by December 20, and projects are anticipated to commence at the start of the Spring 2025 semester.

Discover how U.S. Census Bureau Tools can help you find free data for your research projects, community, and more. See how to access the latest American Community Survey and 2020 Census data for various geographies including New York City and Long Island at data.census.gov. Learn about Community Resilience Estimates and how to navigate My Community Explorer; an interactive map-based tool which highlights demographic and socioeconomic data that measure inequality. This session will involve live demonstrations and hands-on exercises for participants. Registrants will receive the Zoom link one day prior to the event.

Please Register for SBU Libraries' AI Club: Exploring Census Data here.

Abstract: Generative visual models like Stable Diffusion and Sora generate photorealistic images and videos that are nearly indistinguishable from real ones to a naive observer. However, their grasp of the physical world remains an open question: Do they understand 3D geometry, light, and object interactions, or are they mere pixel parrots of their training data? Through systematic probing, I will demonstrate that these models surprisingly learn fundamental scene properties--intrinsic images such as surface normals, depth, albedo, and shading (à la Barrow & Tenenbaum, 1978)--without explicit supervision, which enables applications like image relighting. But I will also show that this knowledge is insufficient. Careful analysis reveals unexpected failures: inconsistent shadows, multiple vanishing points, and scenes that defy basic physics. All these findings suggest these models excel at local texture synthesis but struggle with global reasoning: a crucial gap between imitation and true understanding. I will then conclude by outlining a path toward generative world models that emulate global and counterfactual reasoning, causality, and physics.

Bio: Anand Bhattad is a Research Assistant Professor at the Toyota Technological Institute at Chicago. He earned his PhD from the University of Illinois Urbana-Champaign in 2024 under the mentorship of David Forsyth. His research interests lie at the intersection of computer vision and computer graphics, with a current focus on understanding the knowledge encoded in generative models. Anand has received Outstanding Reviewer honors at ICCV 2023 and CVPR 2021, and his CVPR 2022 paper was nominated for a Best Paper Award. He actively contributes to the research community by leading workshops at CVPR and ECCV, including Scholars and Big Models: How Can Academics Adapt? (CVPR 2023), CV 20/20: A Retrospective Vision (CVPR 2024), Knowledge in Generative Models (ECCV 2024), and How to Stand Out in the Crowd? (CVPR 2025). For more details, visit https://anandbhattad.github.io/


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.

Speaker: Gary Kazantsev (Head of Quant Technology Strategy in the Office of the CTO at Bloomberg)

 

Date/Time: Friday, October 15, 2021 10:00AM-11:00AM EST

 

Title: Machine Learning in Finance

Abstract: Machine learning is changing our world at an accelerating pace. In this talk we will discuss the recent developments in how machine learning and artificial intelligence are changing finance, from a perspective of a technology company which is a key  participant in the financial markets. We will give an overview and discuss the evolution of selected flagship Bloomberg ML and AI projects, such as sentiment analysis, question answering, social media analysis, information extraction and prediction of market impact of news stories. We will discuss practical issues in delivering production machine learning solutions to problems of finance, highlighting issues such as interpretability, privacy and nonstationarity. We will also discuss current research directions in machine learning for finance. We will conclude with a Q&A session.

Bio: (https://www.techatbloomberg.com/people/gary-kazantsev/) Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company's Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.

Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.

He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.


Join Zoom Meetinghttps://stonybrook.zoom.us/j/93374426887?pwd=cE9zeW51VXFEN2R0YnNPbHF1WFp0Zz09Meeting ID: 933 7442 6887Passcode: 330347One tap mobile+16468769923,,93374426887# US (New York)+13126266799,,93374426887# US (Chicago)Dial by your location +1 646 876 9923 US (New York) +1 312 626 6799 US (Chicago) +1 301 715 8592 US (Washington DC) +1 346 248 7799 US (Houston) +1 408 638 0968 US (San Jose) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma)Meeting ID: 933 7442 6887

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.us/j/98738234619?pwd=djJFQXBWbkpmblZDT25zNlVMYWpCQT09

Meeting ID: 987 3823 4619
Passcode: 474618

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Learning Generalizable Program and Architecture Representations for Performance Modeling

Abstract: Performance modeling is an essential tool in many areas of computer science and engineering. However, existing performance modeling approaches have limitations, such as high computational cost, narrow flexibility, or restricted accuracy/generality. To address these limitations, this talk introduces PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional and independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling-related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general and efficient than previous approaches. This talk will also introduce how PerfVec's design principles can benefit broader research areas.

Biography: Lingda Li is a computer scientist at Brookhaven National Laboratory. He is generally interested in computer architecture and programming model research, with focus on simulation/modeling, memory systems, and machine learning. Before joining BNL, he worked at the Department of Computer Science of Rutgers University as a postdoc to carry out GPGPU research. He obtained a PhD in computer architecture from the Microprocessor Research and Development Center at Peking University.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605837856?pwd=kYqJs4bVBt4E0cMCWR6GXH3wxzOoiw.1

Meeting ID: 160 583 7856
Passcode: 161580

Title: Cyberinfrastructure for forward prediction and inversion estimation with uncertainty quantification

Seminar Speaker: Dr. Mengyang Gu, Assistant Professor, Department of Statistics and Applied Probability, University of California, Santa Barbara

Abstract: In this talk, we introduce four useful tools for forward prediction and inversion estimation. The first tool is the parallel partial Gaussian process surrogate model for emulating expensive computer simulations with massive coordinates. The tool is implemented in the RobustGaSP package available in R, MATLAB, and Python, for predicting both scalar- and vector-valued outputs with uncertainty assessment. The second tool is implemented in the RobustCalibration package, which handles Bayesian data inversion or model calibration by one or multiple types of experimental observations. A unique feature of the package is the inclusion of fast surrogate models of both scalar- and vector-valued computer simulations that bypass the expensive simulation in one line of code. The third tool is implemented in the AIUQ package, available in both R and MATLAB. In this approach, we show that differential dynamic microscopy, a scattering-based analysis tool that extracts dynamical information from microscopy videos, is equivalent to fitting the temporal auto-covariance in Fourier space, based on a latent factor model we construct. We develop a more efficient estimator and reduce the computational cost to pseudolinear order with respect to the number of observations without approximation, by utilizing the generalized Schur algorithm for the Toeplitz covariance. In the last tool, we developed a new method called the inverse Kalman filter, which enables fast matrix-vector multiplication between a covariance matrix from a dynamic linear model and any real-valued vector with a linear computational cost. These new approaches outline a wide range of applications that include emulating expensive simulation at molecular-, meso- and macro-scales, active learning with error control, nonparametric estimation of particle interaction functions, and data inversion from microscopy and velocity fields.

Join Zoom Meeting: https://bnl.zoomgov.com/j/1606285496?pwd=2yJYSG6lx8gMPiibzgAIBQtKHIjuHV.1
Meeting ID: 160 628 5496
Passcode: 472506

Abstract: Spectroscopy and imaging are two primary tools for probing material structures. However, the discovery of trends that guide the design of improved materials is often hindered by intertwined physical interactions or significant experimental noise. In this talk, I will present machine learning approaches that address both challenges. The first part focuses on the interpretation of X-ray absorption spectroscopy (XAS). We developed a controlled projection algorithm, RankAAE, which disentangles coupled structural descriptors in complex datasets and reveals analysis rules for inferring new structural information visually from spectra. The second part targets transmission electron microscopy (TEM) imaging of material structures. We developed a machine learning model capable of denoising extremely noisy images, while demonstrating strong out-of-distribution generalization. I will describe the construction of these models and demonstrate their effectiveness through representative scientific case studies.

Bio: Dr. Xiaohui Qu is a Staff Scientist in the Theory and Computation Group at the Center for Functional Nanomaterials (CFN), Brookhaven National Laboratory. His research focuses on developing interpretable machine learning and data analytics methods for materials science, with an emphasis on extracting structural insights from X-ray absorption spectroscopy and transmission electron microscopy. Dr. Qu earned his B.S. in Environmental Engineering and Ph.D. in Environmental Science from Shandong University, China, followed by postdoctoral research in Physics at Nanyang Technological University, Singapore, in Chemistry at Universidade Nova de Lisboa, Portugal, and in Materials at Lawrence Berkeley National Laboratory.

Location: IACS Seminar Room


Event Details & Calendar Link (includes zoom info): https://calendar.stonybrook.edu/site/iacs/event/iacs-seminar-speaker--xiaohui-qu-brookhaven-national-lab/