Abstract: Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into \textit{why} a computation fails. We introduce a white-box method: \textbf{Circuit-based Reasoning Verification (CRV)}. We hypothesize that attribution graphs of correct CoT steps, viewed as \textit{execution traces} of the model's latent reasoning circuits, possess distinct structural fingerprints from those of incorrect steps. By training a classifier on structural features of these graphs, we show that these traces contain a powerful signal of reasoning errors. Our white-box approach yields novel scientific insights unattainable by other methods. (1) We demonstrate that structural signatures of error are highly predictive, establishing the viability of verifying reasoning directly via its computational graph. (2) We find these signatures to be highly domain-specific, revealing that failures in different reasoning tasks manifest as distinct computational patterns. (3) We provide evidence that these signatures are not merely correlational; by using our analysis to guide targeted interventions on individual transcoder features, we successfully correct the model's faulty reasoning. Our work shows that, by scrutinizing a model's computational process, we can move from simple error detection to a deeper, causal understanding of LLM reasoning.

Speaker: Xianjun Yang

Location: Old Computer Science Building - CS2311
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.
Prof. Eugene A. Feinberg, from the Department of Applied Mathematics and Statistics, presents, Recent Developments in Markov Decision Processes Relevant to AI on April 4 at 4p. The talk discusses recent developments in Markov Decision Processes potentially relevant to artificial intelligence. These developments include complexity estimations for exact and approximate algorithms, decision making with incomplete information and multiple criteria, and continuity properties of optimal values and expectations. Dr. Eugene A. Feinberg is currently Distinguished Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He is an expert on applied probability, stochastic models of operations research, Markov decision processes, and on industrial applications of operations research and statistics. He has published more than 150 papers and edited the Handbook of Markov Decision Processes. His research has been supported by NSF, DOE, DOD, NYSTAR (New York State Office of Science, Technology, and Academic Research), NYSERDA (New York State Energy Research and Development Authority) and by industry. He is a Fellow of INFORMS (The Institute for Operations Research and Management Sciences) and has received several awards including 2012 IEEE Charles Hirsh Award for developing and implementing smart grid technologies, 2012 IBM Faculty Award, and 2000 Industrial Associates Award from Northrop Grumman. Dr. Feinberg is an Associate Editor for Mathematics of Operations Research and for Applied Mathematics Letters. He is an Area Editor for Operations Research Letters. Refreshments will be provided
Language shared online through social media or messaging reflects people's thoughts and emotions. Processing this data with Natural Language Processing (NLP) and machine learning can reveal mental health and psychological traits. For example, analyzing Facebook posts enables me to predict depression before it is clinically diagnosed and highlight particular symptoms. At the population level, billions of geo-tagged Tweets can be used to monitor health risk patterns, including depression and anxiety trends across communities. Beyond assessment, I'm using Large Language Models (LLMs) to improve mental health care, including training therapists and assisting with Cognitive Behavioral Therapy. These applications of NLP and Al may lead to earlier and more effective interventions and improved access for underserved populations. Speaker: Johannes Eichstaedt, Ph.D. Assistant Professor, Psychology & Human-Centered Al, Stanford University

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

Abstract: Humans perceive the world through global structures such as parts, branches, and their spatial arrangement. Most deep learning models, however, operate mainly at the pixel level. This disconnect between local and global understanding limits interpretability and control. In this thesis, we explore topology as a mathematical framework for bridging local predictions and global structure in dense prediction and generation tasks. We first incorporate topological constraints into semantic segmentation to preserve anatomical relationships and improve multi-class consistency. We next develop structure-level uncertainty estimation, producing more interpretable and actionable measures of model error over branches and connections rather than isolated pixels. Then, we introduce a topology-guided diffusion framework for controllable image generation using structural attributes such as object count and connectivity. Finally, we extend image generation to the longitudinal task, where we aim to capture structural changes across timepoints. All these contributions together establish topology as a unifying interface for building dense prediction models that are structurally aware, interpretable, and controllable.

Speaker: Saumya Gupta

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/97950688136?pwd=NCa3XOsgIaMIsTVlQBQJ11n27NzL8s.1
Meeting ID: 979 5068 8136
Passcode: 941798

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/
AI can help you write, you hear. AI can save you time, leverage your skills, enhance your productivity. . . . But you also hear: AI output is not reliable, not adequate for advanced tasks/learning, not ethical to use -- you could get in deep trouble for using AI tools without adequate mastery and caution. Which way is it?
Come join this hands-on workshop where you will explore AI tools and their affordances. Engage in writing tasks to learn how to use AI tools effectively and responsibly.
Sign up for a seat now: https://docs.google.com/forms/d/e/1FAIpQLSd0iDTKkTYnkxFd4LkgqbtP97zQSS4FI_MiPVm7p6IY5SGwSg/viewform