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.

Abstract: Designing custom proteins could revolutionize medicine and materials, but it remains an immense scientific challenge. Our work uses large-scale AI foundation models to generate novel proteins tailored to bind specific small molecules. Each AI-generated design is passed through a rigorous, multi-stage validation pipeline to ensure it is biophysically realistic. A key innovation is fine-tuning our model with data from molecular dynamics (MD) simulations, exposing it to the conformational dynamics and energetics of protein-ligand binding. This physics-aware training results in novel protein designs with enhanced stability and more effective binding capabilities.

Bio: Xin Dai is an Assistant Computational Scientist in the Artificial Intelligence Department of the CDS. His work centers on AI for Science with a strong focus on computational biology. He earned his PhD in Physics from Tsinghua University.

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.

Location: CDS, Bldg. 725, Training Room

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

Meeting ID: 160 438 3624
Passcode: 558449


Are you concerned about AI issues with your asynchronous online courses? Is your fully online course vulnerable to AI plagiarism? Do you want to engage your online students using AI? Discover the future of education with our AI-powered solutions designed specifically for online asynchronous courses. This innovative approach uses artificial intelligence to transform the way courses are delivered, making learning more personalized, engaging, and effective.

Register here.
TITLE: Sampling Using Langevin Diffusions Beyond the Worst-Case by Andrej Risteski of CMU


ABSTRACT: Many tasks involving generative models involve being able to sample from distributions parametrized as p(x) = e^{-f(x)}/Z where Z is the normalizing constant, for some function f whose values and gradients we can query. This mode of access to f is natural -- for instance sampling from posteriors in latent-variable models. Classical results show that a natural random walk, Langevin diffusion, mixes rapidly when f is convex. Unfortunately, even in simple examples, the applications listed above will entail working with functions f that are nonconvex.

We exhibit instances where Langevin diffusion (combined with other tools) can provably be shown to mix rapidly in instances of relevance in practice: distributions p that are multimodal, as well as distributions p that have a natural manifold structure on their level sets. 

Stony Brook University Libraries invites students, faculty, & staff to join a conversation about how AI is transforming the private sector workforce. As AI tools move from experimentation to everyday business use, companies are rethinking roles, skill sets, leadership, and long-term strategy. This discussion-based event will focus on the fast-paced changes and directions at tech companies and their possible impact. This event will be particularly relevant for students preparing for an AI influenced job market and how to position themselves for opportunities in a rapidly evolving professional landscape.

The discussion will be led by Tariq Khan, Senior Director of Private Cloud Solutions at Hewlett Packard Enterprise. Tariq is a technology leader and architect with experience across private cloud, hybrid cloud, and data center platforms. He is responsible for shaping the technology architecture and strategic direction of HPE's Private Cloud offerings across on premises and cloud integrated environments.

Light refreshments will be served.


Location: Melville Library, NRR, Learning Lab

This workshop is intended for researchers, practitioners, students, and industry professionals in AI, robotics, machine learning, human-robot interaction, and related fields.

Workshop Overview:

Instead of learning from data alone, an embodied AI system learns through its movements, sensors, and interactions with the environment. This form of active, experience-based learning, informed by ongoing self-evaluation of its own abilities, enables embodied AI systems to adapt on the fly, understand context rather than just commands, and collaborate with humans in more natural and trustworthy ways.

Workshop Goals:

  1. Foster interdisciplinary dialogue across AI, robotics, and cognitive science.
  2. Identify key challenges and future research directions in embodied intelligence.
  3. Examine the role of embodiment in advancing toward AGI.

This workshop is Invitation-only. Please email Dr. IV Ramakrishnan (ram@cs.stonybrook.edu) to attend.

Read the announcement: https://mcusercontent.com/237207911c0fd4c1f78dd8524/files/070dec2e-a2f5-143e-0fe2-c4ebecdb5193/Embodied_AI_Workshop_Invitation_.pdf

Abstract: In recent years, we have been developing generative AI methods to design increasingly complex objects. Our goal is to improve performance while ensuring that these objects remain controllable. This requires addressing several challenging problems, including:
  • Modeling composite objects in a way that preserves consistency under deformation.
  • Estimating the uncertainty of the surrogate models used to predict performance during optimization.
  • Co-designing objects for both high performance and ease of control.
In this talk, I will present our approach to these challenges and describe end-to-end design pipelines that have the potential to radically transform computer-assisted engineering.

Speaker: Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996, where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist.
His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and machine learning. He has (co)authored over 400 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of the IEEE journal Transactions For Pattern Analysis and Machine Intelligence. He often serves as the program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies.
IACS and the Dept of Ecology and Evolution invite you to a PRODiG+ Fellowship Seminar.

Computational Approaches to Understanding and Forecasting Biodiversity Responses to Global Change

Summary: Advancing biodiversity forecasting requires developing computational approaches that better represent ecological complexity. In this talk, I will discuss how my research integrates field observations, predictive modeling, machine learning, and spatiotemporally explicit environmental data to develop more mechanistic and reliable forecasting tools.

Speaker: Dr. Anna Thonis, a postdoctoral researcher in NYU's Winchell Lab and Founder and Co-Chair of the IUCN SSC Anoline Lizard Specialist Group, studies how anthropogenic change reshapes the distributions of reptiles. Combining field and quantitative modeling methods, her work focuses on the ecology and conservation of Puerto Rican and urban Anolis lizards. Her website can be accessed here.

Location: IACS Seminar Room, and via Zoom.

Zoom: https://stonybrook.zoom.us/j/97023584426?pwd=HrnrYeYJfVUhaS8iOKjWF3Tbh8HYS1.1&jst=2
ID: 97023584426
Passcode: 241762

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.

Abstract: This presentation will begin by outlining key challenges facing the modern power grid and summarizing our group's research efforts to address them. It will then discuss how AI and machine learning are reshaping the grid modernization. The major focus of the talk will highlight a range of AI/ML applications we have developed in recent years to enhance grid operation, planning, control, and security.

Biography: Meng Yue is currently leading the Grid Modernization and Security Group in the Interdisciplinary Science Department at Brookhaven National Laboratory (BNL). He received his Ph. D. from Michigan State University in electrical engineering. His major research interests include power system modeling, simulation, and control, and applications of AI/ML- and quantum machine learning and quantum computing in operation, planning, and security of the future grid.

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.

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