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. Unless otherwise noted, all meeting will be held on Fridays at 1:20 p.m. in Love 106.
Fall 2025
08/29/2025
Organizational Meeting
09/05/2025 -- Remote
Speaker: Elias Döhrer (TU Chemnitz)
Title: Incorporating Self-Repulsion into Riemannian metrics
Abstract: Inspired by the work of Michor, Bauer, Bruveris, Maor et al. on the manifold of immersed curves, we propose a new strong Riemannian metric on the manifold of (parametrized) embedded curves of H^s regularity, s in (3/2,2). The construction is motivated by the concept of tangent-point energies, a family of self-avoiding functionals on curves and surfaces of arbitrary dimension. Their notion of self-repulsion allowed us to capture the topological property „embeddedness“ in a continuous way. This talk illustrates the impact of this metric and highlights its most important features, namely metric and geodesic completeness, relative compactness of bounded sets with respect to the weak H^s topology, and existence of length-minimizing geodesics between every pair of curves in the same knot class. https://arxiv.org/abs/2501.16647
09/12/2025
Speaker: Caroline Moosmüller (University of North Carolina at Chapel Hill)
Title: Learning in the space of probability measures
Abstract: Many datasets in modern applications - from cell gene expression and images to shapes and text documents - are naturally interpreted as probability measures, distributions, histograms, or point clouds. This perspective motivates the development of learning algorithms that operate directly in the space of probability measures. However, this space presents unique challenges: it is nonlinear and infinite-dimensional. Fortunately, it possesses a natural Riemannian-type geometry which enables meaningful learning algorithms. This talk will provide an introduction to the space of probability measures and present approaches to unsupervised, supervised, and manifold learning within this framework. We will examine temporal evolutions on this space, including flows involving stochastic gradient descent and trajectory inference, with applications to analyzing gene expression in single cells. The proposed algorithms are furthermore demonstrated in pattern recognition tasks in imaging and medical applications.
09/19/2025 -- Joint Seminar with the SC Artificial Intelligence Seminar
Speaker: Rocío Díaz Martín (FSU)
Title: The Barycentric Coding Model in Optimal Transport
Abstract: The context of this seminar will be the theory of Optimal Transport and its applications. We will consider the problem of estimating a point, either a distribution or a network, under the Barycentric Coding Model with respect to the Wasserstein (W) or Gromov-Wasserstein (GW) distance functions. Specifically, assuming that the target belongs to the set of W or GW barycenters of a finite collection of known templates, we aim to estimate the unknown barycentric coordinates with respect to those templates. In other words, the goal is to determine the right combination of templates (or barycentric coordinates) that best reconstructs the target. From the perspective of harmonic analysis, computing barycenters can be seen as a 'synthesis problem', whereas retrieving their coordinates corresponds to solving an 'analysis problem'. We will review the general theory for the classical case, i.e., using the Wasserstein metric, and then delve into the Gromov-Wasserstein case. For the latter, we focus on algorithms for finding barycentric coordinates (analysis), leveraging existing techniques for constructing barycenters (synthesis) that rely on fixed-point iteration (G. Peyré, M. Cuturi, and J. Solomon, 2016) and differentiation approaches via a blow-up method (S. Chowdhury and T. Needham, 2020). Applications will include covariance estimation, classification, compression, and data imputation.
09/26/2025
Speaker: Cagatay Ayhan (FSU)
Title: TBA
10/03/2025
Speaker: Ali Kara (FSU)
Title: TBA
10/10/2025
Speaker: Kun Meng (FSU Statistics)
Title: TBA
10/17/2025
Speaker: Mario Gomez Flores
10/24/2025 -- Joint Seminar with the SC Artificial Intelligence Seminar
!!!Special Location and time: 499 Dirac Science Library at 12:00pm!!!
Speaker: Gordon Erlebacher (FSU Scientific Computing)
Title: Latest Architectures, Hierarchical Reasoning Model
11/07/2025 -- Academic Job Market Discussion
Speakers: Rocío Díaz Martín (FSU), Ali Kara (FSU), Zhe Su (Auburn), Zezhong Zhang (Auburn)
11/14/2025
Speaker: Mao Nishino
11/21/2025
Speaker: Wenwen Li