University Libraries Presents: The SBU Libraries AI Club hosts their third AI Club Icebreaker for new students during WOW. Come see several projects the AI Club has been working on, how you can participate, and how you can join us!

Location: Melville Library Galleria

Register here!


Abstract: Human gaze behavior is a fundamental cue for understanding social intent, human-machine interaction, and cognitive processes. This dissertation addresses the challenges of gaze target estimation (GTE), also known as gaze following, by developing a holistic understanding of gaze across complex environments.
First, we improve GTE performance through Patch-level Distribution Prediction (PDP). Unlike traditional pixel-wise regression, PDP models gaze as a spatial distribution over patches, better accounting for annotation variance and regularizing the pixel-wise heatmap prediction through multi-scale modeling. Second, to mitigate the high cost of data labeling, we present 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 via a diffusion model, GCDR achieves robust performance with minimal human annotation. Third, we expand 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 and predicting gaze targets across views, we explore a new direction that overcomes single-view limitations such as face occlusion and out-of-view targets. Finally, we propose OmniGF, a multi-person gaze following model built on Vision-Language Models (VLMs) that enriches gaze target localization with semantic and social reasoning. By leveraging the semantic capabilities of VLMs alongside structural innovations to ground the model with fine-grained cues for each individual, OmniGF achieves state-of-the-art performance across three gaze following tasks.
Collectively, by tackling the gaze following problem through the distinct yet complementary perspectives of probabilistic modeling, geometric reasoning, and multimodal learning, this dissertation builds a holistic understanding of human gaze, paving the way for more intuitive artificial intelligence systems in downstream applications.

Speaker: Qiaomu Miao

Location: NCS 220

This workshop synthesizes the latest research on the impact of AI usage in education so that you could make informed decisions on whether and how to use AI to facilitate your learning. You might have seen conflicting reports on whether the use of AI is good for learning. In this workshop, we are going to tease out, drawing on the latest research, which types of AI usage are beneficial or harmful for different kinds of learning. At the end of the workshop, you should walk away with more clarity on when and how to use AI for your own learning. Join PRODIG+ fellow on critical AI, Zheng Fu, in this informative workshop.

Register for this Zoom workshop.

Abstract: Language is not just something we generate, but something humans use to interact with the world around them. Indeed, today's conversational AI agents speak fluently, but often treat language as prediction rather than interaction, producing responses that sound correct while failing to recognize when requests are ungrounded, impossible, or misunderstood. My research asks what it would take for multimodal agents to take the next step and use language effectively to take actions in grounded contexts and in this talk, I argue that many challenges in multimodal LLM design, including alignment, hallucination, and adaptation, are due to a lack of pragmatics: an understanding of the implicit context behind the implied actions of the words in the query. From visual understanding, to automatic speech recognition, to hallucination detection, I will demonstrate that incorporating pragmatic/contextual reasoning substantially improves agent behavior, and that pragmatic reasoning will drive a necessary shift in how we build multimodal conversational agents that can see, listen, act, and speak in context.


Bio: David M. Chan, Ph.D., is a postdoctoral scholar at the University of California, Berkeley, specializing in multimodal conversational AI. His research focuses on developing scalable AI systems that move beyond language prediction toward grounded language use, integrating vision, audio, and language to enable pragmatic interaction, improve AI-human collaboration, and reduce hallucinations in generative models. Beyond academia, he has worked with leading organizations including Amazon, Google, and NASA, to build and deploy safe, efficient, and accessible machine learning systems. He is also the developer and maintainer of TSNE-CUDA, an open-source tool for high-dimensional data visualization, used by over 40,000 researchers in fields ranging from biomedical technology to industrial manufacturing. David holds a Ph.D. and M.Sc. in Computer Science from UC Berkeley, where he was a graduate fellow with the Center for Technology, Society & Policy (CTSP), and dual B.Sc. degrees in Computer Science and Mathematics from the University of Denver.

Location: NCS 120

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.

AI for Neutrino Oscillation Fits

Abstract: Neutrino oscillation experiments face the problem of performing likelihood fits in a very highdimensional space to extract the oscillation parameters from measured spectra. The current strategy for this is to fix all but a few parameters, reducing the dimensionality of the fit to a manageable number, but this risks missing correlations between the parameters, which can impact the systematics of the measurement. This is an area where artificial intelligence and machine learning could make great improvements. I will discuss the problem, explain how it is currently dealt with, and sketch one possible way of implementing AI to solve it, using a sampling method combining Smolyak's algorithm, for efficient sampling using sparse grids, with an adaptive grid refinement to increase sampling in regions that are more likely to contain the global minimum.

Speaker: Steven Linden is a physicist in the Instrumentation Department at BNL working on neutrino and dark matter experiments. He got his PhD from Yale in 2010 doing analysis on the MiniBooNE experiment and then worked on various dark matter detectors (MiniCLEAN, Pico, SENSEI) at SNOLAB in Canada for nearly ten years before moving to BNL.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1614473319?pwd=e4QSSgFHqDzHx870ixJpwuG3yqBere.1

Meeting ID: 161 447 3319
Passcode: 733283

Learn how to prompt AI to help clean datasets and write formulas in Google Sheets.

When you have a messy dataset, it can take a lot of time to clean it up before you can start analyzing. Can AI help? In this workshop, we'll collect live data and then use Gemini AI (the stand alone tool) to help clean up the data. Then, we'll use it to help do some analysis. Because we'll be working with live data live in Gemini, we don't know exactly what will happen, but that's the reality of data and data cleaning!

In this session, you will

  1. Craft effective AI prompts to generate Google Sheets formulas for data analysis and manipulation
  2. Utilize Gemini to develop regular expression formulas to extract, reformat, clean text-based data
  3. Develop formulas for numerical analysis using Gemini AI

https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_dht1o3rNzlZhHka?source=event+manager&session=0815250900sheets

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)





https://stonybrook.zoom.us/j/91775729097pwd=Qlc5Nks0NmlyKzJwMjR0S0hrdVZ3QT09

Meeting ID: 917 7572 9097
Passcode: 555459


Abstract: As the saying goes, there are many ways to skin a cat.
While we don't want to go around skinning cats, the world of
optimization is rich with different problems, problem formulations,
and methods and approaches, each with different guarantees and
computational benefits. In this talk we will take a tour down the
problem of structured sparsity in sensing to see how one simple
problem can inspire a wide range of analysis and tools. First, I will
present the optimality conditions for a generalized structured sparse
problem, which can be geometrically visualized as alignment of vectors
and matrices. Then I will introduce three approximation methods for
the problem of phase retrieval, which are a twist on stochastic
gradient and coordinate descent methods. These methods leverage
fundamental numerical linear algebra concepts to give fast approximate
solutions to large-scale problems, which then after postprocessing can
produce more reliable sensing results.

Bio: Yifan Sun received her PhD in Electrical Engineering from the
University of California Los Angeles in 2015, with research focusing
on convex optimization and semidefinite programming. She was then
Technicolor Research and Innovation, focusing on machine learning and
data science applications. More recently, she completed two postdocs,
at the University of British Columbia in Vancouver, Canada and
L'Institut National de Recherche en Informatique et Automatique
(INRIA) in Paris, France.