As artificial intelligence continues to transform higher education and the world beyond, how are students engaging with this change? Join us for a student-led discussion that explores how AI is influencing academic integrity, learning practices, and students' perspectives on its role in future workplaces.

Our panelists will share their experiences and reflections on questions such as:
1. What counts as appropriate and inappropriate use of AI in coursework?
2. How do faculty approach AI and talk about its implications in class?
3. What does AI mean for students' learning and ethical decision-making?
4. How are students building their understanding of AI tools and their potential uses in professional contexts?

This conversation offers an authentic look at how students are navigating the promises and challenges of AI--both in their studies and as they look ahead to applying these technologies responsibly in their fields.

Register here.

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.

Embodied Intelligence at Scientific User Facilities

Abstract: This presentation explores the active work integrating artificial intelligence and robotics at the National Synchrotron Light Source II, and a perspective for the future. Through various case studies, we highlight the optimization of operations, improved experimental outcomes, and the orchestration of distributed multimodal experiments. This ongoing development includes collaborators from across the light and neutron sources in the DOE complex. We will elaborate on the open-source Bluesky project, and its capabilities to support adaptive and autonomous experiments. Additionally, we will discuss how Bluesky can be integrated with open-source robotic control software to unlock new flexible automation for autonomous scientific research, which scales to new experiments and continues to leverage human ingenuity.

Biography: Dr. Phillip M. Maffettone is an Associate Computational Scientist in the Data Science and Systems Integration Division at NSLS-II. His research focuses on accelerating scientific discovery at user facilities through the integration of robotics, artificial intelligence (AI), and advanced experiment orchestration systems. He leads the N3XTware project, constructing the software architecture for the next 12 beamlines to be built at NSLS-II. Prior to this he built the brain on the world's first mobile robotic scientist at the University of Liverpool, and later spearheaded the machine learning platform for a biotechnology start-up, BigHat Biosciences. He holds a DPhil in Inorganic Chemistry from the University of Oxford and a B.S. in Chemical Engineering from the University at Buffalo.

Location: CDS, Bldg. 725, Training Room

Link: https://bnl.zoomgov.com/j/16049713 31?pwd=nc5CV3cOFrdYxordFieP W07tIDmwYb.1

Meeting ID: 160 497 1331
Passcode: 289875

Abstract: Large language models (LLMs) may exhibit unintended or undesirable behaviors. Recent works have concentrated on aligning LLMs to mitigate harmful outputs. Despite these efforts, some anomalies indicate that even a well-conducted alignment process can be easily circumvented, whether intentionally or accidentally. Does alignment fine-tuning yield have robust effects on models, or are its impacts merely superficial? In this work, we make the first exploration of this phenomenon from both theoretical and empirical perspectives. Empirically, we demonstrate the elasticity of post-alignment models, i.e., the tendency to revert to the behavior distribution formed during the pre-training phase upon further fine-tuning. Leveraging compression theory, we formally deduce that fine-tuning disproportionately undermines alignment relative to pre-training, potentially by orders of magnitude. We validate the presence of elasticity through experiments on models of varying types and scales. Specifically, we find that model performance declines rapidly before reverting to the pre-training distribution, after which the rate of decline drops significantly. Furthermore, we further reveal that elasticity positively correlates with the increased model size and the expansion of pre-training data. Our findings underscore the need to address the inherent elasticity of LLMs to mitigate their resistance to alignment.

Speaker: Huajian Zhang

Location: CS2311
Join us at the Center for Excellence in Learning and Teaching (CELT) for an interactive Zoom workshop on Generative AI designed for faculty and staff interested in enhancing teaching and assessment practices, increasing student engagement, and navigating the rapidly evolving landscape of AI tools. Participants will be introduced to common AI tools, explore potential instructional uses, and discuss key considerations such as academic integrity, transparency, and equity.
Register now: https://stonybrook.zoom.us/meeting/register/6js1eP64T1ys8tyU57EJ7Q#/registration
The INS (International Neuroethics Society) AI and Consciousness Affinity Group is hosting a talk titled Bringing Trustworthiness in Generative AI and Agentic AI Using Thought Knowledge Graphs featuring speaker Manas Gaur, a computer scientist at UMBC.
The talk will examine the interplay between Thought Knowledge Graphs (TKGs) and how they can form more trustworthy and reasoning-based responses in AI. They will also discuss introducing novel methods on implementing TKGs and their overall impact on creating more trustworthy AI systems.
The talk will be held online via Zoom on Monday, December 2 at 1:00pm (EST).
Register to attend.

Abstract: Modern large language models (LLMs) increasingly operate not on single inputs, but over sets of information--collections of generated statements, retrieved documents, or intermediate reasoning steps. However, existing frameworks largely treat these elements independently, overlooking the fact that correctness and reliability often emerge at the set level, where interactions such as contradictions, redundancy, or joint relevance become critical. In this talk, I argue that reasoning over sets is a fundamental challenge for building reliable and controllable LLM systems, and present a unified perspective centered on set-level verification, retrieval, and editing.

Bio: Jay-Yoon Lee is an associate professor in the Graduate School of Data Science at
Seoul National University. The goal of his research is injecting knowledge/constraints into neural models, primarily for natural language processing (NLP) tasks. He is broadly interested in structured prediction, multi-task learning, logical reasoning, and better representation learning for the aforementioned topics. Currently, he is exploring these topics at the Graduate School of Data Science (GSDS), Seoul National University (SNU), with SKI-ML lab members. Prior to joining SNU he was a postdoc associate at IESL with Professor Andrew McCallum and during his Ph.D. with his advisor Jaime Carbonell.

Location: NCS 120



Abstract: The current approach to materials design, driven by strategic experimentation and supported by physics-based simulation across relevant scales, has been the standard for decades. While the theoretical component in this workflow provides valuable understanding of material behavior, it often fails to deliver actionable guidance for implementation. Advances in artificial intelligence and machine learning (AI/ML), together with high-performance computing (HPC), now offer a viable pathway to close this gap and accelerate both discovery and process optimization. This presentation will outline practical approaches for integrating AI/ML with HPC-enabled, high-throughput computation to explore high-dimensional search spaces. Examples will include the development of engineering alloys for extreme environments, the use of neural networks to rapidly improve computational thermodynamic models, and vapor processing optimization for the manufacturing of ultra-high-temperature ceramics. I will highlight how scientific insight and domain expertise remain essential for translating surrogate model predictions into impactful outcomes. Finally, I will conclude with current challenges and future opportunities for AI/HPC-driven materials research.

Speaker: Dongwon Shin
This seminar will be held in person and online

Join Zoom Meeting: https://stonybrook.zoom.us/j/93730374357?pwd=YDLJ7ELqOQnTZEQhlN8Pa4TuhaiFK8.1

Understand Prompting the crucial part to interface with models

Discover how to prompt effectively by exploring the details behind your AI interactions. This isn't just about basic prompting; it's about understanding how to articulate your ideas clearly. We'll showcase a few prompts and how they work. Discover how giving AI the right details can truly boost your productivity and help you reclaim valuable time in your day.

In this session, you will

  1. Utilize AI models effectively
  2. Understanding different prompts
  3. Find out tips that we use with AI

Register: https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_dht1o3rNzlZhHka?source=event+manager&session=0805251000ai
Defending Software Systems from Cyber Attack Campaigns Presented by R. Sekar The DNC hack of 2016, the Equifax breach of 2017, and the spate of ransomware campaigns in 2019 demonstrate the formidable challenges we face in securing our network and software systems against highly stealthy and sophisticated adversaries. In this talk, I will describe two avenues of research we have been pursuing to help tilt the table against such powerful adversaries. The first is software hardening techniques that make software vulnerabilities harder to exploit. To maximize their applicability and ease of use, our techniques are implemented into compilers, or they directly transform binary code. I will outline some of the exciting new developments we have had in this area over the years, including randomization, memory safety, information-flow tracking, control-flow integrity, and code-pointer integrity. We complement this first line of defense with techniques for analyzing and understanding attack campaigns that manage to slip past all deployed defenses. Our techniques can sift through logs consisting of hundreds of millions of events to zoom in on attack activity that may span just a few hundred events. I will describe our experience in mapping out several DARPA-sponsored red team attack campaigns.
2026 MIC Symposium on Multilingualism | Featured Talk

Searching for Common Ground in Human-AI Interaction (What Gen AI Can't Do)

Abstract: For many of us, the alien invasion of LLMs has been unsettling, at best. Who knew that monstrous quantities of text and compute power could simulate human discourse processing? But despite claims about emergent theory-of-mind in reasoning models, LLMs remain stochastic parrots: they do not collaborate, project co-presence, form conceptual pacts, accurately signal commitment to what they say, or accrue common ground. This lack of pragmatic competence is clear from our recent studies of narrative and referential communication by AI vs. humans.

Speaker: Susan Brennan is SUNY Distinguished Professor at Stony Brook University in Cognitive Science within the Department of Psychology, with affiliations in Computer Science and Linguistics. Her research uses eye-tracking, experimental, behavioral, and observational methods to study the psycholinguistics of communication (verbal, nonverbal, and multimodal), adaptive language use, and the human use of technology, including spoken dialogue systems. She currently directs the NSF-funded Research Traineeship project, Detecting and Addressing Bias in Data, Humans, and Institutions, a collaboration among PhD. programs in Cognitive Science, Social & Health Psychology, Computer Science, Applied Math & Statistics, Linguistics, Political Science, Economics, Africana Studies, and Women's Studies. This traineeship supports students' AI and bias-related convergent research projects that include assessing potential for LLVMs (Large Language-Vision Models) to act as interlocutors in spontaneous dialogue as well as collaborating with the Innocence Network about AI tools. She holds a Ph.D. in cognitive psychology from Stanford University, an M.S. in visual studies from MIT (from what is now known as the MIT Media Lab), and a B.A. from Cornell University in cultural anthropology. She has worked in industry at Atari Research, Hewlett-Packard Labs, and Apple Computer, as well as at NSF as a program officer for the Graduate Research Funding Program.

Faculty Response Panel:
  • Jiwon Hwang (Panel Chair; Assistant Professor, Asian and Asian American Studies)
  • Rose Tirotta-Esposito (Director, Center for Excellence in Learning and Teaching)
  • Lav Varshney (Director, AI Innovation Institute)
  • Josh Zhang (Associate Professor, Sociology and Institute for Advanced Computational Science)




Location: Humanities 1006