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.
Spring 2026
01/16/2026
Organizational Meeting
01/23/2026 -- UF-FSU Geometry and Topology Meeting
No Seminar talk
01/30/2026
Speaker: Shu Liu (FSU)
Title: A generative approach for simulating Wasserstein geometric flows
Abstract: Wasserstein geometric flows (WGFs) constitute a class of time-evolution partial differential equations that play a fundamental role in modeling and simulating physical systems. In this talk, we present a sampling-friendly, optimization-free approach for simulating WGFs by leveraging generative models from deep learning. Specifically, we project the WGF defined on the probability manifold onto a finite-dimensional parameter space induced by the generative model, in a manner that faithfully mimics the dynamics of the original geometric flow. The resulting system of ordinary differential equations, referred to as the parametrized WGF, can then be efficiently solved using classical numerical integration methods. This framework enables direct generation of samples from the time marginals of the WGFs, even in high-dimensional settings. In addition, we establish error analysis that provides accuracy guarantees for the proposed method. The talk will conclude with a brief discussion of future research directions and potential applications.
02/06/2026
Speaker: Eric Kubischta (FSU)
Title: TBA
Abstract: TBA
02/13/2026 -- Joint Seminar with the FSU-NC State PDE Day
Speaker: Ryan Murray (NC State)
Title: A variational approach to studying dimension reduction algorithms
Abstract: Dimension reduction algorithms, such as principal component analysis (PCA), multidimensional scaling (MDS), and stochastic neighbor embeddings (SNE and tSNE), are an important tool for data exploration, visualization, and subgroup identification. While these algorithms see broad application across many scientific fields, our theoretical understanding of non-linear dimension reduction algorithms remains limited. This talk will describe new results that identify large data limits for MDS and tSNE using tools from the Calculus of Variations. We'll highlight connections with Gromov-Wasserstein distances, manifold learning, and Perona-Malik diffusion. Along the way, we will showcase situations where standard libraries give outputs that are misleading, and propose new computational algorithms to mitigate these issues and improve efficiency.
02/20/2026
Speaker: Ferhat Karabatman (FSU)
Title: TBA
Abstract: TBA
02/27/2026 (Zoom Talk)
Speaker: Javier Gómez-Serrano (Brown)
Title: TBA
Abstract: TBA
03/06/2026 (Zoom Talk)
Speaker: Tryphon Georgiou (UC Irvine)
Title: TBA
Abstract: TBA
03/13/2026
Speaker: Rafiq Islam (FSU)
Title: TBA
Abstract: TBA
03/27/2026 -- Special Session on Geometric Methods for Data Science at the 2026 Spring Southeastern Sectional Meeting
No Seminar talk
04/03/2026-- Joint Seminar with the SC Artificial Intelligence Seminar
!!!Special Location and Time: 499 Dirac Science Library at 12:00pm!!!
Speaker: TBA
Title: AI for Scientific Discovery
Abstract: TBA
04/10/2026
Speaker: Washington Mio (FSU)
Title: TBA
Abstract: TBA
04/17/2026
Speaker: Pan Fang (FSU)
Title: TBA
Abstract: TBA
04/24/2026
Speaker: Ece Karacam (FSU)
Title: TBA
Abstract: TBA