Abstract: Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines. However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve--entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models. In this work, we propose s3, a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over naïve RAG. s3 requires only 2.4k training samples to outperform baselines trained on over 70 × more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks.

Speaker: Peter Zeng

Location: CS2311
Communication-Efficient Heterogeneity-Aware Machine Learning System and Architecture by Xuehai Qian

ABSTRACT: The key success of deep learning is the increasing size of models that can achieve high accuracy. At the same time, it is difficult to train the complex models with large data sets. Therefore, it is crucial to accelerate training with distributed systems and architectures, where communication and heterogeneity are two key challenges. In this talk, I will present two heterogeneity-aware decentralized training protocols without communication bottleneck. Specifically, Hop supports arbitrary iteration gap between workers by novel queue-based synchronization which can tolerate heterogeneity with system techniques. Prague uses randomized communication to tolerate heterogeneity with a new training algorithm based on partial reduce -- an efficient communication primitive. If time permits, I will present the systematic tensor partitioning for training on heterogeneous accelerator arrays (e.g., GPU/TPU). We believe that our principled approaches are crucial for achieving high-performance and efficient distributed training.

BIO: Xuehai Qian is an assistant professor at University of Southern California. His research interests include domain-specific systems and architectures, performance tuning and resource management of cloud systems and parallel computer architectures. He received his PhD from the University of Illinois Urbana Champaign and was a postdoc at UC Berkeley. He is the recipient of W.J Poppelbaum Memorial Award at UIUC, NSF CRII and CAREER Award, and the inaugural ACSIC (American Chinese Scholar In Computing) Rising Star Award.

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.

#1 How to train your Scientific Chatbot by Alexandr Prozorov, Post-Doctoral Research Associate


Abstract: RHIC is closing its 25-year run with ~1 EB of data and decades of hard-won know-how that risk drifting into obscurity. The RHIC Data & Analysis Preservation Plan (DAPP) pilots an AI assistant that lets physicists talk to RHIC in natural language--searching internal notes, code, workflows, and docs, and pointing to runnable, containerized analyses. Built on Retrieval-Augmented Generation(RAG) with a Model Context Protocol orchestration layer, the system indexes heterogeneous, experiment-specific content and enforces role-aware access
for public vs. collaboration-restricted materials. Takeaway: domain-adapted AI can turn a legacy exabyte into reproducible answers, training assets, and new discovery paths.

Biography: Alexandr Prozorov is a postdoc from Czech Technical University in Prague working in STAR experiment. Fascinated by AI

#2 Quantum AI: Atoms, Cavities and Learning by Raman Kumar, Post-Doctoral Research Associate, Instrumentation Department

Abstract: The Instrumentation Department (IO) in the Discovery Technologies directorate at BNL is engaged in exploring various aspects of quantum systems research. One of the main goals of our group's effort is in developing neutral atom-cavity array platforms for remote entanglement generation and distributed quantum processing. This platform promises to herald truly scalable quantum computing systems and open new paradigms for networking and sensing. In this talk, I will explain our group's research and the role AI is playing in unlocking new insights with two examples. The first application of AI is in fabrication process prediction of micro-cavity structures. The second application revolves around role of AI in quantum error detection and correction in modern quantum computing systems.

Biography: Dr. Raman Kumar is a postdoctoral research associate in the IO department at BNL working with Dr./Prof. Sebastian Will (Columbia U.). Kumar obtained his Ph.D. degree in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign. Prior to joining BNL in Nov 2024, Kumar worked as a postdoc at the City College in New York working on topological photonic quantum sensing using NV centers in diamond. Kumar and Will combined have an extremely wide moat and expertise in a variety of different areas which include Ultra cold atoms and molecules, quantum optics, quantum condensed matter, nanofabrication, semiconductor devices and advanced electromagnetics. Their areas of research interest include scalable quantum computing, communications and sensing, all enabled by AI.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting https://bnl.zoomgov.com/j/1607892208?pwd=MSjxN5btSeToZsQMwEQzCCbBo5h58V.1

Meeting ID: 160 789 2208
Passcode: 753871

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks 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.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room

Speakers

Jianda Chen, EBNN - Improving the stability and accuracy of PDE-ML hybrid AGCMs

Boyang Li, CDS - Accelerating Materials Discovery using Machine Learning

Jaehye on Do, NPP Isotopes - Using LLMs for Isotopes Research and Production

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382

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


The Program in Writing and Rhetoric
Invites you to
A Rhetorical/Deliberative Framework for AI Language Model Alignment
featuring
Prof Zoltan Majdik Professor
North Dakota State University
In this talk, Prof. Majdik proposes a framework for aligning LLMs with values grounded in the norms of rhetorical culture and deliberative democracy. Alongside long-standing AI alignment value targets like safety and transparency, this AI alignment framework assesses to what extent a language model exhibits human and humane values that foster communicative engagement, and it codifies approaches to tuning existing models to better align with such values.

Location: Humanities 1008
Talk by Zhenhua Liu to be followed by AI Institute updates


Abstract: Decision making with uncertainty has been studied in multiple communities extensively. Recently, online optimization has gained popularity partially because of its promising performance guarantees by incorporating predictions. In this talk, I will provide an overview of our work on algorithm designs for online optimization and its applications. Then, I will talk about our recent work in ACM Sigmetrics 2019 on choosing predictions and control algorithms simultaneously and dynamically. Finally, I will discuss some ongoing efforts and collaboration opportunities.

Bio: Zhenhua Liu is currently an assistant professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He is also affiliated with the Department of Computer Science, the AI Institute and the Smart Energy Technology Cluster. He received his PhD degree in Computer Science from California Institute of Technology. His current research interests include cloud computing, online optimization and learning, smart grid, market design and distributed control. His research combines rigorous analysis and system design, and goes from theory, to prototype, and eventually to industry to make real impacts.

As AI drives rapid change across professional fields, how do you bring these developments into your classroom? The CELT AI Panel Discussion will gather academic thought leaders to explore how generative AI is reshaping teaching, learning, and the knowledge students need for today's world. Our panelists will share practical strategies for integrating AI-related advancements into course content, highlight both opportunities and challenges, and discuss how educators can help students build critical thinking, ethical awareness, and hands-on experience with emerging AI technologies. Join us to examine how teaching can evolve alongside an AI-transformed society.

Register here.

The Challenges of Machine Learning in Adversarial Settings by Patrick McDaniel, Pennsylvania State University

Abstract: Advances in AI and machine learning have enabled new applications and services to interpret and process inputs in previously unthinkable complex environments. Autonomous cars, data analytics, adaptive communication and self-aware software systems are now revolutionizing markets by achieving or exceeding human performance. In this talk, I consider the evolving use of machine learning in security-sensitive contexts and explore why many systems are vulnerable to nonobvious and potentially dangerous manipulation. Here, we examine sensitivity in any application whose misuse might lead to harm--for instance, forcing adaptive network in an unstable state, crashing an autonomous vehicle or bypassing an adult content filter. I explore the use of machine learning in this area particularly in light of recent discoveries in the creation of adversarial samples and defenses against them and posit on future attacks on machine learning. The talk is concluded with a discussion of the technological and societal challenges we face as a result of current and future advances in intelligent computing.

Bio: Patrick McDaniel is the William L. Weiss Professor of Information and Communications Technology and Director of the Institute for Networking and Security Research in the School of Electrical Engineering and Computer Science at the Pennsylvania State University. Professor McDaniel is also a Fellow of the IEEE and ACM and the director of the NSF Frontier Center for Trustworthy Machine Learning. He also served as the program manager and lead scientist for the Army Research Laboratory's Cyber-Security Collaborative Research Alliance from 2013 to 2018. Patrick's research centrally focuses on a wide range of topics in computer and network security and technical public policy. Prior to joining Penn State in 2004, he was a senior research staff member at AT&T Labs-Research.