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: Finding Your Inner Qubit  — Quantum Computing 101

Abstract: Quantum computing has a reputation for being mysterious, but the basic model is simple: quantum states are complex vectors, gates are unitary matrices, and measurement produces random outcomes with probabilities determined by squared inner products. This talk introduces qubits and quantum circuits with minimal physics, using the Bloch sphere (the complex projective line CP^1) as a visual aid for single-qubit states, gates, and measurements. We will discuss why we need quantum mechanics at all, what is meant by a “quantum speedup,” and highlight a few canonical quantum algorithms that achieve provable advantage over classical algorithms. We will also cover the main obstacles to building large-scale quantum computers today, and time permitting, we will outline what “quantum machine learning” usually refers to and why practical advantages remain an open question.

• 02/13/2026 -- Joint Seminar with the FSU-NC State PDE Day and with the SC Artificial Intelligence Seminar 
  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: Geometric Perspective on Concentration Phenomena in Frame Theory
  Abstract: Frames are fundamental structures in many areas, and tight frames are particularly valued for their stability and robustness properties. In this work, we establish concentration phenomena for Parseval frames, i.e. tight frames with frame bound 1, under isotropic distributions supported on the sphere and the Euclidean ball, showing that epsilon-nearly Parseval frames are prevalent in these probabilistic models. We further introduce a distinguished subclass of Parseval frames and prove that they are both robust under the Bernoulli-type erasure model and prevalent within the space of Parseval frames. As an application of our results, we derive a high-probability upper bound of order o(epsilon d) for the Paulsen problem.

• 02/27/2026 (Zoom Talk)
  Speaker: Javier Gómez-Serrano (Brown)
  Title: Mathematical Exploration and Discovery at Scale
  Abstract: Machine learning is transforming mathematical discovery, enabling advances on longstanding open problems. In this talk, I will discuss AlphaEvolve, a general-purpose evolutionary coding agent that uses large language models to autonomously discover old and new mathematical constructions and potentially go beyond them. AlphaEvolve tackles a wide variety of problems across analysis, geometry, combinatorics, and number theory. In some instances, AlphaEvolve is also able to generalize results for a finite number of input values into a formula valid for all input values. Furthermore, we are able to combine this methodology with Deep Think and AlphaProof in a broader framework where the additional proof-assistants and reasoning systems provide automated proof generation and further mathematical insights. This illustrates how general-purpose AI systems can systematically successfully explore broad mathematical landscapes at an unprecedented speed, leading us to do mathematics at scale.

• 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