Abstract: Formalization of mathematics is the process by which pen-and-paper mathematics is translated into a strict chain of logical deductions down to the axioms of mathematics. The subject has seen renewed interest in the last decades thanks to the development of computer systems called proof assistants, which make this feasible in practice.
There have now been several examples of high-profile mathematical results which have been formalized. In principle, any mathematical domain is accessible. However, existing projects are skewed towards algebra instead of analysis. Notable exceptions are a project which formalized enough of Gromov's convex integration theory to deduce Smale's sphere eversion theorem and the ongoing project to formalize Carleson's convergence theorem for Fourier series.
This workshop will bring together formalization experts and interested mathematicians to give a new impulse to formalization of analysis (in a very broad sense), and to develop abstractions and tools to deduplicate effort.

Application Information: ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate. Some funding may be available for travel and lodging. Graduate students who apply must have their advisor submit a statement of support in order to be considered.

The deadline to apply for this workshop is January 24, 2026.

https://icerm.brown.edu/program/topical_workshop/tw-26-ttfa

Scaling the NY AI Innovation Ecosystem

The State University of New York at Stony Brook will bring together leading AI experts to promote a future where AI drives responsible progress. This two-day event will provide a significant opportunity to explore the future of AI, exchange ideas, and connect with those at the forefront of research and deployment. We invite faculty, staff, and students from all SUNY institutions and beyond, as well as industry AI practitioners and policymakers to attend.

Recognized AI experts from academia, industry, and government will present on topics such as AI applications, innovative developments in research and technology, workforce development, as well as ethical and societal impacts.

A 90-minute poster session is included in the schedule. If you would like to submit an abstract for consideration, please see the Call for Abstracts. The poster session segment of the symposium will be held in honor of the Inauguration of Dr. Andrea Goldsmith, the State University of New York at Stony Brook's seventh President. Poster printing for all participants will be covered by the Inauguration Planning Committee. SUNY students presenting posters are also eligible for travel reimbursement.

We kindly ask faculty to encourage their students to attend and to submit their work for presentation.

For additional information and to register, visit the symposium website. Please direct any questions to suny-ai-symposium-sbu@stonybrook.edu.

Register.

The Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop.

This workshop will focus on topics including big data analytics and machine learning with Spark, as well as deep learning.

This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event. SBU's Institute for Advanced Computational Science (IACS) is one of those satellite sites!

Location: IACS Conference Room #2

Interested applicants must first have an ACCESS ID. If you don't have the ID, please visit this page to create one: ACCESS USER REGISTRATION.


Once you have an ACCESS ID, please login (see top right here) then register here.
The Division of Educational & Institutional Effectiveness is excited to host International Love Data Week at SBU, February 9-13, 2026!
Join us for a mix of 30-minute virtual sessions, an in-person kickoff on Monday, and a student-focused event on Wednesday celebrating data and data-informed decision-making.
Wrap up the week at the Love Data Week Open House on Friday, 2/13 with light refreshments, data-themed swag, photos with Wolfie, and time to connect with presenters.
Learn more and register on https://www.stonybrook.edu/commcms/oee/recognition/Love%20Data%20Week%202026%20Save%20the%20Date%20Placeholder.php
Abstract: Large language models are prone to memorizing some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users. There is hope that model alignment---a standard training process that tunes a model to harmlessly follow user instructions---would mitigate the risk of extraction. However, we develop two novel attacks that undo a language model's alignment and recover thousands of training examples from popular proprietary aligned models such as OpenAI's ChatGPT. Our work highlights the limitations of existing safeguards to prevent training data leakage in production language models.

Speaker: Pegah Alipoormolabashi

Location: CS2311
Briana Last

As the use of artificial intelligence tools in healthcare increases at a dramatic rate, the conversation about usage has focused on efficiency and innovation. But in a new article published in Nature Mental Health, Stony Brook University’s Briana Last argues that a more pressing question has been largely overlooked: who gets to decide how AI is used in mental healthcare, and in whose interests are those decisions made?

Ann Kirschner writes, "Colleges and universities are a public good—or we’re supposed to be. While the challenge is real, so is the price of paralysis. AI won’t wait for anyone’s permission to reshape learning and work. The question isn't whether we'll change—it's whether we'll be relevant when we do. That 'A' in AI? It can still stand for anxiety. Better if it stands for action. The choice, for now, is still ours."