University Libraries Presents

In a digital landscape flooded with hyper-realistic AI images and manipulated media, telling fact from fiction has never been harder. This workshop introduces the art of slow looking, teaching you how to pause, carefully analyze visual evidence, and critically evaluate the authenticity of what you see online.

Register here to join

The Provost's Office is excited to invite you to join in responding to an extraordinary opportunity to enhance our academic and research capabilities in AI at Stony Brook. SUNY recently made funding available to support the creation of departments of AI and Society at its universities. Stony Brook is well-positioned to seize this opportunity to build upon our interdisciplinary strengths in AI.

The office is hosting a forum on Friday, Nov. 15, from 11:30 a.m. to 1:30 p.m., in Ballroom A, SAC. You are invited to attend to learn more about this opportunity and to help us generate ideas to build a compelling proposal for Stony Brook to submit to SUNY. Lunch will be provided.

Please click here to RSVP as soon as possible.

This funding will support innovation in our curriculum, allowing us to create programs that explore the social and societal impact of AI alongside the technological advancements led by researchers in engineering and scientific disciplines.

We believe we can make a significant impact through this SUNY program and look forward to your participation in this initiative.
Abstract: Many scientific and engineering challenges, such as the design of materials or molecules or the control of experimental systems, rely on the existence of fast predictive models that can evaluate potential designs or control policies. Traditionally this has been accomplished through numerical simulation; more recently data-driven machine learning methods have been applied. However, both approaches leave gaps: physical modeling can be accurate and extrapolates well to previously-unstudied conditions, but it is often computationally expensive and relies on physics approximations that may not be valid. Machine learning can generalize from massive amounts of real-world or simulation data, but suffers from physical grounding and extrapolation into new regimes, as well as in settings where large data sets do not exist.
In this talk I explore an intermediate regime, which is hybrid reduced order models: fast simplified physics approximations where some of the unknown or approximated equations are replaced with data-driven machine learning components. Examples include coarse-grained models where the full macroscopic equations cannot be derived from first-principles microscopic equations, multiscale models with unknown closure terms or sub-grid parameterization schemes, and low-order or latent dynamical systems that learn governing equations on a low-dimensional reduced state space. I discuss how such reduced systems can be identified from very limited data, much less than is often needed in traditional machine learning but at much lower time-to-solution than traditional numerical modeling. This facilitates not only system design and control but also uncertainty quantification approaches that search the space of possible equations for predictive models that can explain the data. I will focus on an example from materials science concerning the design of self-assembling block copolymer nanomaterials.

Speaker: Dr. Nathan Urban, Applied Mathematics Department, Brookhaven National Laboratory

Location: Laufer 101

Zoom: https://stonybrook.zoom.us/j/96090260834?pwd=mw8QTHbMOw9oeU9hazZeoq8bN4VIfH.1
Meeting ID: 960 9026 0834 Passcode: 374969
The Antonija Prelec Memorial Committee in collaboration with Stony Brook University Libraries are very excited to bring you the 2019 Prelec Memorial Lecture! This year, we are pleased to announce our speaker is Patricia Flatley Brennan, RN, PhD, Director of the National Library of Medicine.

No registration required. Find more information here.
Join the Conversation: Share Your Thoughts about Learning, Academics, and AI

The world of college is changing fast, and Artificial Intelligence (AI) is at the center of it. We are part of the Institute on AI, Pedagogy, and the Curriculum with AAC&U, and we need to hear from the people AI affects most: you!

This is an open discussion for all students to share their honest experiences, their top concerns, and their best ideas about AI in our academic environment. We'll be diving into these key questions:
  • How can AI actually make learning better or easier? What opportunities do you see for using AI tools to enhance your assignments, research, or skills?
  • What are your biggest worries about AI? Is it about cheating, being graded fairly, or preparing for the job market? How is AI impacting your workload or stress levels?
  • What specific tools, workshops, or policies would help you use AI responsibly and successfully? (Think training, software, or clear rules.)
Date: Monday, December 1st
Time: 12:30pm-1:45pm
Location: West Campus - Location TBD
or
Date: Wednesday, December 3rd
Time: 10:30am-11:45am
Location: East Campus - HSC 2-154B

Please register in advance so we can confirm the room.

Note: Videos will not be shared publicly and comments will only be shared in aggregate.

Your voice matters. Come tell us how AI is affecting your studies, your stress, and your success!
  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)
  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)
  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)
  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)
  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)
CSE 656 Seminar in Computer Vision The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors. Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.
Abstract: Reward hacking, where a reasoning model exploits loopholes in a reward function to achieve high rewards without solving the intended task, poses a significant threat. This behavior may be explicit, i.e. verbalized in the model's chain-of-thought (CoT), or implicit, where the CoT appears benign thus bypasses CoT monitors. To detect implicit reward hacking, we propose TRACE (Truncated Reasoning AUC Evaluation). Our key observation is that hacking occurs when exploiting the loophole is easier than solving the actual task. This means that the model is using less effort than required to achieve high reward. TRACE quantifies effort by measuring how early a model's reasoning becomes sufficient to obtain the reward. We progressively truncate a model's CoT at various lengths, force the model to answer, and estimate the expected reward at each cut-off. A hacking model, which takes a shortcut, will achieve a high expected reward with only a small fraction of its CoT, yielding a large area under the reward-vs-length curve. TRACE achieves over 65% gains over our strongest 72B CoT monitor in math reasoning, and over 30% gains over a 32B monitor in coding. We further show that TRACE can discover unknown loopholes during training. Overall, TRACE offers a scalable unsupervised approach for oversight where current monitoring methods prove ineffective.

Speaker: Dikshya

Location: Old Computer Science Building - CS2311
The GE Vernova Advanced Research Center invites you to the 2026 AI EDGE Symposium.

This collaborative and hands-on learning experience connects 300+ industry thought leaders in the fields of AI, Edge, Robotics, Cybersecurity, and Controls & Optimization.


Attendees will learn about the latest challenges and innovations with luminaries from key government agencies, industry customers, and technology partners as they discuss the latest advancements and trends in these fields.

Register here.