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.
Are you concerned about AI issues with your asynchronous online courses? Is your fully online course vulnerable to AI plagiarism? Do you want to engage your online students using AI? Discover the future of education with our AI-powered solutions designed specifically for online asynchronous courses. This innovative approach uses artificial intelligence to transform the way courses are delivered, making learning more personalized, engaging, and effective.

https://stonybrook.zoom.us/meeting/register/tJMvd-irqTotGtQONZqerPf_TnhXcx8t2sA1

In this workshop, we will take a historical journey through the ideas that eventually led to today's large language models, such as ChatGPT and Claude.

Modern LLMs can seem overwhelmingly complex, but they didn't appear all at once. Many of the basic ideas behind them have been developing for more than a century. By following this history step by step, we can better understand what language models do and how they evolved.

In part 1 of this workshop, we will begin with Andrey Markov's early work on sequences and probability in 1913 and Claude Shannon's influential work on information and language in 1948. From there, we will explore key ideas such as Markov chains, N-grams, and the next-word generation process using simple, accessible examples.

In part 2 of the workshop, which is not in this workshop, we will also look at how an artificial neuron can learn to predict the next word, an important step toward neural language models that later became the foundation of modern LLMs and we will see what perplexity is.

The workshop is designed for anyone who is curious about where today's AI came from and how a century of ideas about language, probability, and computation helped make modern language models possible. This workshop targets students of any level who are interested in language models or history. No background in computer science or artificial intelligence is required. The hardest part is converting counted numbers to probabilities.

Our historical journey will cover roughly the period from 1918 to 1970s.

Location: Melville Library, Central Reading Room, Lab A

Register for the event on SB Engaged.

This virtual presentation series is designed to inform the Stony Brook University research community about the Research Funding Landscape of key topic areas. Our Strategic Research Initiatives team will provide insight into the rapidly shifting funding environment using policy briefs, budgetary priorities, and relevant legislation. We will highlight federal and state priorities in the current and upcoming years to help Stony Brook researchers develop strategies for pursuing funding in a rapidly shifting environment. This series is moderated by Mónica Bugallo, Interim Vice President for Research & Innovation.

Join us for the third in the series, focused on the artificial intelligence landscape:


Translating the Funding Landscape for Stony Brook Researchers: Artificial Intelligence
Presented by Catherine Chen, Ph.D., Research Development Associate
Faculty Respondent: Assistant Professor Nav Nidhi Rajput, Department of Materials Science and Chemical Engineering
Wednesday, April 22, 2026 at 2 pm to 3 pm

Registration is Required

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

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.

HPCortex - a new, general-purpose machine learning library for HPC

Abstract: I will introduce HPCortex, a lightweight, C++, MPI-native machine-learning library for heterogeneous HPC systems. It implements many common architecture patterns including transformers, graph neural networks, and convolutional networks, and delivers performance portability across NVIDIA, AMD, and Intel GPUs while depending only on MPI and standard compiler/BLAS stacks. I will illustrate its capabilities via a surrogate model for the RHIC AGS Booster digital twin, a simple GNN for a coupled spring system, and a compact language model, then outline the roadmap.

Biography: Christopher is a research scientist and head of the Scientific Computing Applications Group in the Computational Science Department at Brookhaven National Laboratory. Previously he was an assistant staff scientist in the Physics Dept. at Columbia University, and held physics postdoctoral research positions at both Brookhaven and Columbia. He earned his Ph.D in Theoretical Physics from the University of Edinburgh, UK.
His scientific background is in lattice QCD and high performance computing, but since joining Brookhaven in 2020 his research interests have expanded to include machine learning, applied mathematics and performance analysis, with a particular emphasis on building tools to support scientific research on HPC systems.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604143373?pwd=hHT2yaIjahBIQ6tieURFqs8Pwex9gU.1

Meeting ID: 160 414 3373
Passcode: 277410

Synthetic Dreams and Ghost Machines

Dr. Steven Skiena will join the Cinema for a presentation exploring the rapidly evolving worlds of artificial intelligence and robotics.

Dr. Skiena is Distinguished Teaching Professor of Computer Science and Associate Director of the AI Innovation Institute at Stony Brook University. A leading researcher in data science and algorithms, he is the author of several influential books on AI and computation, including The Algorithm Design Manual and The Data Science Design Manual.

The presentation will be followed by a screening of Ex Machina, Alex Garland's provocative science-fiction thriller exploring the seductive and unsettling boundaries between human consciousness and artificial intelligence.

Abstract: AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and real-world use. Typical agent behavior unfolds sequentially, where each action requires an API call that can incur substantial latency. For example, a game of chess between two state-of-the-art agents can take hours. We introduce speculative actions, a lossless acceleration framework for general agentic systems. Inspired by speculative execution in microprocessors and speculative decoding in LLM inference, our method uses faster models to predict likely future actions and executes them in parallel, committing only when predictions match. We evaluate speculative actions across gaming, e-commerce, and web search environments, and additionally study a lossy extension in an operating systems setting. Across domains, we achieve up to 55% next-action prediction accuracy, translating into substantial latency reductions. Finally, we present a cost-latency analysis that formalizes the tradeoff between speculative breadth and time savings. This analysis enables principled tuning and selective branch launching, to ensure multi-branch speculation delivers practical speedups without prohibitive cost growth.

Speaker: Kehan Qi

Location: Old Computer Science Building - CS2311