The Data Science and Machine Learning Seminar as a regular seminar in the Department of Mathematics at the Florida State University. Beginning in Fall 2025, the seminar will collaborate with the Artificial Intelligence Seminar of the Department of Scientific Computing. Together, they will host two joint talks: one in the Department of Mathematics and one in the Department of Scientific Computing.
The organizers of the Data Science and Machine Learning Seminar are Martin Bauer and Tom Needham. Please contact one of the organizers if you have any questions and/or wish to give a talk in our seminar.
A schedule of the upcoming semester can be found below. An archive of schedules of previous semesters can be found here. Unless otherwise noted, all meeting will be held on Fridays at 1:20 p.m. in Love 106. More information on some of the talks can be found here.
Fall 2026
08/28/2026
Organizational Meeting
09/01/2026 --- Using AI in Mathematical Research I: Joint Talk on with the ACM Seminar
!!!Special Location, Day and Time: Tuesday, 3:05pm in Lov 231!!!
Speaker: Sanghyun Lee
Title: AI-Assisted Computational Mathematics Research Workflows, Boundaries, and Verification
09/11/2026 --- AI and Mathematics (Zoom)
Speaker: Max Weinreich
Title: The crisis of AI-generated mathematics
Abstract: In this talk, I present the case for total opposition to the use of artificial intelligence in mathematics. I offer proposals for how individuals, departments, journals, and institutions can act in concert to make sure that mathematics survives the coming crisis.
Registration: https://www.math.fsu.edu/~bauer/AI-Math/weinreich-register.html
09/18/2026
09/25/2026 --- Panel-Discussion "Mathematics at a Turning Point? AI, Research, and the Next Generation." (Zoom)
Speakers: Ettore Aldrovandi (FSU), Jeremy Avigad (CMU), Tony Feng (Berkeley), Javier Gomez-Serrano (Brown), Alex Kontorovich (Rutgers, University), Rachel Ward (UT Austin)
Mandatory Registration: https://www.math.fsu.edu/~bauer/AI-Math/turning-point-2026.html
10/02/2026 --- The AI Crisis in Math: A Perspective from Philosophy of Science
Speaker: Zina Ward (FSU)
Abstract: Mathematical advances by AI systems over the last six months have prompted worried reflection on the future of mathematical inquiry. Many of the questions mathematicians are discussing deal with topics that philosophers of science have long been interested in, such as scientific change, the nature of mathematical knowledge, how knowledge production is organized, and the (multiple, competing) aims of research. In this talk, I’ll introduce a few philosophical ideas that may be useful for thinking about mathematics’ current predicament, with the aim of continuing the conversation unfolding during this semester’s colloquium series.
10/09/2026 --- Formalisation of mathematics: insight into an ongoing revolution (Zoom)
Speaker: Michael Rothgang (Bonn University)
Abstract: Digitising or formalising mathematics refers to translating mathematical arguments from natural language (e.g. English) into a computer-readable format: this makes it possible to verify proofs with certainty, often improves mathematical understanding, can transform peer-review and enables new levels of collaboration. In recent years and months, there has been spectacular progress in formalising research-level mathematics in diverse fields. In fact, recent breakthroughs (such as, on the Navier-Stokes equations) have been enabled through formalisation. I will explain what the excitement is about and give an idea of the current state of the art.
To illustrate this point, I will explain the state of the art in differential geometry: there is much more to it than prompting an AI. Already finding the correct general definition is a non-trivial mathematical question. I will survey what is currently formalised, what is being worked on and where you can help. I will also comment on technical challenges of working with manifolds in Lean and how to address them.
Registration: https://www.math.fsu.edu/~bauer/AI-Math/lean-register.html
10/16/2026 --- Using AI in Mathematical Research II
Speaker: Dustin Mixon (OSU)
Title: From prompts to proofs to people
Abstract: In this talk, I’ll share three stories about how my collaborators and I have solved open problems using AI. These experiences capture a core message: AI can provide a key idea beyond your expertise and deliver a Lean-certified proof you can trust, but then leave you struggling to understand why it works. This motivates a more social approach to math research in which we bring AI-generated breakthroughs to the chalkboard and wrestle with the mathematics together.
10/23/2026 --- Joint Seminar with the SC Artificial Intelligence Seminar
!!!Special Location and Time: 499 Dirac Science Library at 12:00pm!!!
Speaker: Nick Dexter (FSU)
Title: AI Interpretability
Abstract: Mechanistic interpretability treats a trained network as something to reverse-engineer rather than a black box to benchmark. A joint session with mathematics on the techniques for working out what internal components compute, and what would even count as an explanation.
10/30/2026
Speaker: Rongjie Lai (Purdue University)
11/06/2026 --- Grad Student Flash Talks: "Geometric Methods for Data Science"
Speaker: TBA
11/13/2026 --- Computational Linguistics
Speaker: Thomas Juzek (FSU)
12/04/2026 --- Optimal Transport
Speaker: Levin Maier (IAS Princeton)