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

Abstract: Foundation models are rapidly becoming capable assistants for knowledge work, but their deployment in real settings is limited by three gaps: they do not transfer reliably across environments, their internal reasoning is opaque, and their behavior is hard to control precisely. In this talk, I argue that these limitations are not only about model size -- they are fundamentally about whether learning captures and leverages the underlying structure of the data-generating process. I use causal thinking as a practical lens to model what is invariant, what changes, and what can be intervened on, and I further show how this leads to learning principles that improve trustworthiness.
I will first present methods for learning unifying mechanisms from heterogeneous data, across domains and modalities, to enable reliable transfer and controllable generation. Next, I will show how structured concepts can be recovered even from seemingly unstructured data, by analyzing and improving self-supervised objectives (such as masking and diffusion) through hierarchical latent-variable models. These concept structures can then be used to interpret generative models and support targeted, multi-level edits. Finally, I connect these two threads to generalization beyond the training distribution. I will discuss natural conditions for extrapolation and a compositional generation framework that improves prompt following for novel concept combinations. I will conclude with a brief outlook on self-improving world models and AI-assisted scientific discovery.


Bio: Lingjing Kong is a Ph.D. candidate in the Computer Science Department at Carnegie Mellon University. His research focuses on Causal AI for transferable, interpretable, and controllable systems, with an emphasis on understanding and exploiting the structure of real-world data to make foundation models actionable and more reliable. He develops identification principles and scalable algorithms for learning unified models from heterogeneous data, uncovering hierarchical concept structures in unstructured data (e.g., images and text), and generalizing beyond training support through compositionality and extrapolation. His work has appeared in top ML venues including ICML, NeurIPS, CVPR, ICLR, and EMNLP and has been prototyped and applied in industry.

Location: NCS 120
West Campus - SAC- Student Activities Center - Ballrooms A & B 100 Nicolls Road Stony Brook NY 11794 Job Fair.jpg The Career Center invites Alumni Employers and Job Seekers to the IT/Computer Science Job and Internship Fair this spring. Job Seekers: A job fair is an opportunity for you to present yourself professionally in person to a potential employer, while showcasing your communication skills. Get more information Alumni Employers: Held in both the fall and spring semesters, this event is ideal for employers looking to fill internship, co-op, part-time and full-time opportunities in the field of information technology (i.e. Software Engineering, Network Administration, Web Development, etc.). Register here to recruit top SBU talent.

The Future Histories Studio at Stony Brook University and Guggenheim New York are collaborating to present a day-long symposium on October 24 at the Simons Center for Geometry and Physics. This conference will explore urgent questions at the intersection of artificial intelligence, machine learning, and the human, and is co-organized by Noam Segal, LG Electronics Associate Curator at Guggenheim New York. In this role, Noam plays an important part in researching these topics, promoting a deeper understanding of the ways in which contemporary artists use new technologies, and developing and supporting the Guggenheim's engagement with technology-based art under the LG Guggenheim Art and Technology Initiative.

The event examines the profound transformations brought by automation--how AI compels us to rethink cognition, agency, and the conditions of reason itself. As these systems become ever more embedded in daily life--largely invisible yet deeply consequential--they challenge the very foundations of subjectivity and governance. We are surrounded by logics we cannot fully access, yet which shape our realities, while new forms of alterity arise--distinct modes of reasoning that propose collective unknowns beyond established frameworks of knowledge.

This emerging terrain invites us to consider cognitive plurality, where biological and technological intelligences generate new categories, concepts, and understandings. Once unique to humans--art, authorship, judgment, invention--are now co-articulated with systems of computation and planetary-scale infrastructure. The symposium brings together artists, scholars, and technologists to probe the cultural, philosophical, and ecological implications of this entanglement.

The concept of neurodiversity has shown that neurological differences such as autism, ADHD, and dyslexia are not deficits but variations that enrich collective life. Extending this to machines can be provocative: just as neurodivergence unsettles fixed definitions of intelligence, so too AI challenges anthropocentric assumptions about cognition. Yet the analogy is limited. Neurodiversity is rooted in the lived struggles of human communities, while machines neither think nor struggle. Human cognition involves perception, learning, memory, and reasoning through embodied experience. Machine cognition, by contrast, is computational pattern recognition and statistical modeling, without consciousness or lived context, and with only narrow forms of sensing.

For this reason, the symposium advances a broader framework of cognitive diversity or technodiversity--a recognition of proliferating intelligences, human, machinic, and hybrid, as part of a shared ecology. This shift calls for new models of creativity, responsibility, and collaboration that honor the irreducibility of human thought while engaging the radical alterity of machine logics.

Location: Stony Brook Simons Center for Geometry and Physics, Della Pietra Family Auditorium

This event is co organized by the Guggenheim New York

Join us at AI Campus Connect for an exciting day filled with all things artificial intelligence. Whether you're an AI pro or just starting out, this event is perfect for anyone interested in AI and how it can boost your career. Come network, sign-up for IBM SkillsBuild, and learn about the latest trends in AI technology. Don't miss out on this opportunity to connect with IBM SkillsBuild experts and expand your knowledge in the world of AI. See you there!

Co-hosted With AI Community and SWE

Location: W0550 Frank Melville, Jr. Memorial Library

Register to attend.
Title:Deep Contextual Modeling for Natural Language Understanding, Generation, and Grounding Zoom instructions: Join Zoom Meeting https://stonybrook.zoom.us/j/645050299?pwd=TVJVRkc3dlhxdDF5d00xWGlDQkovZz09 Meeting ID: 645 050 299 Password: 810247 One tap mobile +16468769923,,645050299#,,#,810247# US (New York) +13126266799,,645050299#,,#,810247# US (Chicago) Dial by your location +1 646 876 9923 US (New York) +1 312 626 6799 US (Chicago) +1 301 715 8592 US +1 346 248 7799 US (Houston) +1 408 638 0968 US (San Jose) +1 669 900 6833 US (San Jose) +1 253 215 8782 US Meeting ID: 645 050 299 Password: 810247 Find your local number: https://stonybrook.zoom.us/u/aemTiJMXu6 Abstract: Natural language is a fundamental form of information and communication. In both human-human and human-computer communication, people reason about the context of text and world state to understand language and produce language response. In this talk, I present several deep neural network based systems that first understand the meaning of language grounded in various contexts where the language is used, and then generate effective language responses in different forms for information access and human-computer communication. First, I will introduce Speaker Interaction RNNs for addressee and response selection in multi-party conversations based on explicit representations for different discourse participants. Then, I will present a text summarization approach for generating email subject lines by optimizing quality scores in a reinforcement learning framework. Finally, I will show an editing-based multi-turn SQL query generation system towards intelligent natural language interfaces to databases. Bio:Rui Zhang is a final year Ph.D. student at Yale University advised by Professor Dragomir Radev. His research interest lies in various natural language processing problems in understanding, generation, and grounding. He has been working on (1) End-to-End Neural Modeling for Entities, Sentences, Documents, and Multi-party Multi-turn Dialogues, (2) Text Summarization for Emails, News, and Scientific Articles, (3) Cross-lingual Information Retrieval for Low-Resource Languages, (4) Context-Dependent Text-to-SQL Semantic Parsing in Human-Computer Interaction. Rui Zhang has published papers and served as Program Committee members at top-tier NLP and AI conferences including ACL, NAACL, EMNLP, AAAI, CoNLL. During his Ph.D., He has done research internships at IBM Thomas J. Watson Research Center, Grammarly Research, and Google AI. He was a graduate student at the University of Michigan and got his bachelor's degrees at both the University of Michigan and Shanghai Jiao Tong University from the UM-SJTU Joint Institute.