Over the past decade, researchers in neuroscience, psychology and artificial intelligence have come together to build advanced computer models that mimic how our brain processes what we see. These models are designed to closely copy the brain's visual system, all the way to a key area called the inferior temporal cortex, which plays an important role in recognizing objects.

Because these computer models can be fully observed, scientists can use them to make detailed predictions about how the brain works -- something older, more theoretical models could not do.

Dr. James DiCarlo's work explores whether these computer digital twin models of the brain could help guide safe, non- invasive ways to infl uence brain activity. In his talk, he explains how such a model could be used to design specific patterns of light. When this carefully designed light is added to what the eye naturally sees, it can precisely influence activity in groups of neurons in the inferior temporal cortex.

Since neural activity in this visual brain area may be connected to emotional states like anxiety, this research could eventually open the door to non-invasive approaches that may benefit mental well-being in the future.

Speaker: James J. DiCarlo, MD, PhD, Peter de Florez Professor, MIT Brain and Cognitive Sciences, and Director, MIT Siegel Family Quest for Intelligence

Location: Staller Center Main Stage

The event will be livestreamed at stonybrook.edu/live

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

This is Stony Brook's quantum moment. Join us for a spotlight on the core achievements and research excellence of faculty across the Colleges of Arts and Sciences (CAS), and Engineering and Applied Sciences (CEAS) - and their collaborative advancements in quantum science and technology. Learn about the real world impact of their enduring work, their leadership in translating foundational science into entrepreneurial opportunities, and their impetus for making connections to next generation innovation.

Presented by: Catherine Chen, Ph.D., Research Development Associate

Welcome remarks: President Andrea Goldsmith

Panel moderators: Dean David Wrobel, CAS, and Dean Andrew Singer, CEAS

Presentations and panel featuring our faculty:

  • Jennifer Cano, CAS, Physics and Astronomy

  • P. Scott Carney, CEAS, Mechanical Engineering

  • Hyeongrak Chuck Choi, CEAS, Electrical and Computer Engineering

  • Eden Figueroa, CAS, Physics and Astronomy

  • Humanshu Gupta, CEAS, Computer Science

  • Angela Kelly, CAS, Physics and Astronomy

Location: Theatre at the Charles B. Wang Center, Stony Brook University

Reserve your tickets by March 26!

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

This workshop synthesizes the latest research on the impact of AI usage in education so that you could make informed decisions on whether and how to use AI to facilitate your learning. You might have seen conflicting reports on whether the use of AI is good for learning. In this workshop, we are going to tease out, drawing on the latest research, which types of AI usage are beneficial or harmful for different kinds of learning. At the end of the workshop, you should walk away with more clarity on when and how to use AI for your own learning. Join PRODIG+ fellow on critical AI, Zheng Fu, in this informative workshop.

Register for this Zoom workshop.

Abstract: Making large language models usable requires post-training methods that align models to human values, robustly handle underspecified inputs, and generalize to diverse instructions. This talk addresses the challenge of developing responsible AI through post-training from the following angles. First, I address contextual robustness. Preference data, for example, is often underspecified, and I show how underspecification in preference data can lead to diverging preferences. Standard reward models fail to properly handle these disagreements, often making decisive choices even when human annotators are split. I argue that more consequential outputs demand more context, and propose clarification question generation as one solution. Second, I will discuss how we can train models to be better instruction followers. I will show that most models severely overfit on a small set of instruction-following constraints and are not able to generalize well to unseen output constraints. I propose to train models with reinforcement learning from verifiable rewards for verifiable instruction following, and show how this leads to improved generalization on constraint following. Throughout the presentation, I will outline how I have applied these insights into developing open generative models, like Tülu and OLMo, and I will conclude with my research agenda for responsible post-training: critical AI evaluation, broader generalization, and expanding what models can reliably do.


Bio: Valentina Pyatkin is a postdoctoral researcher at the Allen Institute for AI and the University of Washington, advised by Prof. Hanna Hajishirzi and Prof. Yejin Choi. Additionally, she is part- time affiliated with the ETH AI Center, where she mentors students and works on post-training for the Swiss AI Initiative. She obtained her PhD in Computer Science from Bar Ilan University. Her work has been awarded an ACL Outstanding Paper Award and the ACL Best Theme Paper Award, and has been supported by a Schmidt Sciences Postdoctoral Award. During her doctoral studies, she conducted research internships at Google and the Allen Institute for AI, where she received the AI2 Outstanding Intern of the Year Award.

Location: NCS 120

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.

Launching a University-Wide AI Innovation Institute:

Last spring, the Office of the Provost led a group of over 30 faculty, staff, and administrators to consider how we can expand and leverage our strengths in AI research and discovery. The resulting recommendation was to launch a university-wide AI Innovation Institute (AI3), which would expand the Institute for AI-driven Discovery and Innovation established in 2018 from a department-level institute within the College of Engineering and Applied Science (CEAS) to the university-wide AI Innovation Institute reporting to the provost.

As a university-wide enterprise, the AI Innovation Institute (AI3) is intended to accelerate, coordinate, and organize AI innovation and education across Stony Brook. The institute will serve to empower the entire university community and beyond, catalyzing core AI research, curriculum innovation, and societal change in the ever-evolving landscape of knowledge work.

The AI Town Hall, led by AI3 Interim Director Skiena, is an open house event that will provide an overview of the major AI initiatives on campus, including the new AI Seed Grant program and Stony Brook's role in New York State's Empire AI program. The session will include time for questions and discussion about the future of AI at Stony Brook.




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

Abstract:

In recent years, the landscape of artificial intelligence (AI) has been reshaped by the rapid emergence of Foundation Models (FMs). These versatile models have garnered widespread attention for their remarkable ability to transcend the boundaries of traditional, bespoke AI solutions and to generalize to a large set of downstream tasks. In this presentation we will describe the development of geospatial FMs with earth observation and weather data and discuss initial results of such models. We will also show how such foundation models can be a new and exciting tool for assisting with and accelerating scientific discovery.

Speaker:

Hendrik Hamann
Distinguished Researcher
IBM T.J. Watson Research Center