Join us for an engaging panel discussion featuring researchers who participated in our inaugural AI JAM session on February 26th. Our panelists will share their firsthand experiences using large language models to tackle complex scientific problems, with a special focus on prompt engineering strategies, discussing both breakthroughs and challenges encountered during this collaborative initiative. Learn how these cutting-edge AI tools are being applied to real-world research questions and discover insights that could inform your own scientific endeavors. Attendees are encouraged to come prepared with questions about prompt engineering for the panel discussion.

Moderator: Adolfy Hoisie, Deputy Director, Computing and Data Sciences

Kevin Yager, Group Leader, AI-Accelerated Nanoscience, Center for Functional Nanomaterials
Lingda Li, Associate Computational Scientist, Systems, Architecture and Computing Technologies, Computing and Data Sciences
Liguo Wang, Director of Scientific Operations, Laboratory for BioMolecular Structure (LBMS), National Synchrotron Light Source II
Weiguo Yin, Physicist, Condensed Matter Theory, Condensed Matter Physics and Materials Science Department

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1606837837?pwd=Tc0mwQqLXpDfYOIaoaurmpLD2mMlzS.1 (Meeting ID)

Passcode: 822553

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.
Abstract: Modern language agents often need to solve tasks requiring long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries. Yet, most LLM systems rely on full-context prompting, appending all past turns regardless of their relevance. This leads to un-bounded memory growth, increased computational costs, and degraded reasoning performance on out-of-distribution input lengths due to LLM forgetting the context. We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant context size when solving long multi-turn tasks. At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning. Leveraging reinforcement learning (RL) and rollout trajectory truncation, we train a MEM1 agent to develop internal states that integrate prior memory with new observations from the environment while strategically discarding irrelevant or redundant information. Experiments across three domains, including internal retrieval QA, open-domain web QA, and multi-turn web shopping, show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on an augmented multi-hop QA dataset with 16 objectives in each task, and generalizes beyond the training horizon. Our results demonstrate the promise of reasoning-driven memory consolidation as a scalable alternative to existing solutions for training long-horizon task-solving agents that involve multiple interactions, where both efficiency and performance are optimized.

Speaker: Yiyang Feng

Location: CS2311

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, November 26, 2024, 12:00 pm -- CDS, Bldg. 725, Training Room

Speakers

Hanfei Yan, NSLS-II

David Park, CDS, AI Dept

Xihaier Luo, CDS, AI Dept

Join Zoom Meeting

https://bnl.zoomgov.com/j/1601052863?pwd=eIX9qZKPGNtQ11uwbK8JP5hIdIxA3V.1

Meeting ID: 160 105 2863

Passcode: 442980


What Does Learning Mean? presented by Jeffrey Heinz

ABSTRACT
When we develop learning algorithms, what computational problems are we solving? In this talk, I discuss different answers that have been proposed for this question, and discuss some of the consequences for machine learning and artificial intelligence. The main lessons I offer are that (1) feasible solutions to learning problems require careful consideration of a target class C of functions, (2) that such a class C cannot include all functions, or even all computable functions, and so many logically possible functions must be outside of C and (3) class C must have significant structure which the solutions take advantage of. These main ideas are motivated and illustrated from modeling language acquisition and the related problem of grammatical inference from example sequences belonging to formal languages.

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.

At our Oct 7 Mixer, BNL's newly minted interim director, John Hill will be present to give opening remarks and kick us off on a new year of impactful scientific AI collaborations.

Abstract: Weather extremes and strong seasonal-to- subseasonal variability pose growing challenges to urban populations, infrastructure, and energy systems. Yet, most cities remain data deserts: routine weather observations are sparse, with stations concentrated at airports rather than within the urban core. This lack of coverage limits our ability to monitor and predict fine-scale urban weather patterns precisely where they matter most. We present a new AI-driven framework for optimal sensor placement and urban weather monitoring. Unlike traditional approaches, our method leverages physics- based simulations together with Bayesian experimental design principles, but does so using a computationally efficient variational inference strategy that makes large-scale optimization tractable. This allows us to guide sensor networks in a way that minimizes information loss while capturing spatiotemporal variability at city scales. Applied to Phoenix, Arizona, our framework outperforms random sensor placement strategies, especially when only a limited number of sensors can be deployed. Importantly, the same AI models that guide sensor placement also function as a real-time nowcasting tool, providing urban weather information over the entire domain, beyond sensor locations. Together, these capabilities offer a scalable pathway to reduce urban data deserts, enhance monitoring of weather extremes, and improve resilience planning for energy, transportation, and public health systems.

Biography: Dr. Katia Lamer is an atmospheric scientist and the Director of the Center for Multiscale Applied Sensing at Brookhaven National Laboratory. Originally from Canada, she earned her B.S. and M.S. in Atmospheric and Oceanic Sciences from McGill University and a Ph.D. in Meteorology from Penn State University. Her research focuses on atmospheric boundary layer processes and remote sensing technologies, with a strong emphasis on data science. At Brookhaven, she is known for her work with the CMAS mobile observatories and its facility that connect fundamental atmospheric science to real-world applications, improving weather prediction, environmental monitoring, and urban climate resilience. Her work has been featured in public outlets such as New Scientist and Wired. Dr. Lamer also serves as an invited member of the World Meteorological Organization's Data Assimilation and Observing Systems Working Group, and the American Meteorological Society's Boundary Layer and Turbulence Committee. puting, communications and sensing, all enabled by AI.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624 | Passcode: 558449

CSE 600 Talk: Haibin Ling - Computer Vision Research and Applications


Abstract: Having been intensively studied over half a decade, computer vision has evolved as a broad research area and become mature in many applications. In this talk, we will summarize our work in computer vision in both core vision topics and application-oriented ones. In particular, for core vision problems, we will report studies on visual tracking, visual matching and visual detection; for applications, we will describe our work on medical image analysis, intelligent transportation, smart projector systems and preliminary work on material property prediction.

Bio: Haibin Ling received the BS and MS degrees from Peking University in 1997 and 2000, respectively, and the PhD degree from the University of Maryland, College Park, in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia. From 2006 to 2007, he worked as a postdoctoral scientist at the University of California Los Angeles. In 2007, he joined Siemens Corporate Research as a research scientist. From 2008 to 2019, he worked as a faculty member of Temple University. In fall 2019, he joined the Department of Computer Science of Stony Brook University where he is currently a SUNY Empire Innovation Professor. His research interests include computer vision, augmented reality, medical image analysis, and human computer interaction. He received the Best Student Paper Award at the ACM UIST in 2003, and the NSF CAREER Award in 2014. He serves as Associate Editor for several journals including IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Pattern Recognition (PR), and Computer Vision and Image Understanding (CVIU). He has served or will serve as Area Chair for CVPR 2014, 2016, 2019 and 2020.

From instant question generation to automated data summaries, learn how to leverage Gemini to simplify your entire Google Forms workflow.

Discover how Gemini can now work in Google Forms. Start by creating a whole Google Form from a prompt. Then, watch the real magic after the data is in: We'll dive into Gemini's ability to analyze and summarize open-ended responses, turning a mountain of feedback into a concise, actionable information. Whether you're gathering student feedback or organizing departmental requests, this session will show you how to let AI do the busy work so you can focus on the results.

In this session, you will:

  1. Generate a Google Form from a prompt
  2. Get question suggestions
  3. Summarize text responses with AI

Register here.