The seminar features talks in all areas of applied and computational mathematics, including applications in science and engineering. Please contact one of the organizers if you are interested in giving a talk.
Unless noted otherwise, the seminar takes place on Tuesdays 1.30–2.30pm in Kemeny 343.
Past seminar talks can be found here.
Organizers: Chris Vales, David C. Freeman
Tue Sep 15, 2026 | 1.30pm | Kemeny 343
Tue Sep 22, 2026 | 1.30pm | Kemeny 343
Tue Sep 29, 2026 | 1.30pm | Kemeny 343
Dynestyx: a probabilistic programming library for dynamical systems
State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning. Despite their importance in both theory and application, dynamical systems have proven difficult to incorporate in modern probabilistic programming languages (PPLs), making state-of-the-art methods less accessible to practitioners and introducing friction in following the "Bayesian workflow." We introduce dynestyx, a probabilistic programming library with first-class support for SSMs, including state-of-the-art methods in the estimation of both states and parameters. Through a single, unified interface, users may specify arbitrary priors for discrete-time or continuous-time dynamical systems, perform inference over mixed-effect data, and make state and parameter estimates with principled uncertainty quantification. We discuss problems, applications, and opportunities that dynestyx introduces.
Tue Oct 6, 2026 | 1.30pm | Kemeny 343
Tue Oct 13, 2026 | 1.30pm | Kemeny 343
Modeling, interpreting, and optimizing functions in biological sequence space
A fundamental goal of genetics is to understand how variation in biological sequences gives rise to differences in measurable characteristics called phenotypes. The mapping from genotype (DNA, RNA or protein sequence) to phenotype can be difficult to model and interpret because the space of possible sequences is enormous and combinations of mutations interact in complex ways. We describe how to use Gaussian process regression to learn genotype-phenotype maps, which to a mathematician are real-valued functions over a discrete space of sequences with a fixed length and alphabet. We will discuss some mathematically nice properties of this modeling framework and their downstream applications for analyzing genotype-phenotype datasets.
Tue Oct 20, 2026 | 1.30pm | Kemeny 343
Title TBA
Abstract TBA.
Tue Oct 27, 2026 | 1.30pm | Kemeny 343
Bridging scales in cell biology: transport, stochasticity, and particle methods
At the scale of a single cell, chemical processes are driven by a complex interplay of spatial transport and stochasticity. Capturing these dynamics requires mathematical models that bridge the microscopic and macroscopic worlds. In this talk, I will introduce the mesoscopic particle methods we use to investigate cellular processes, which we have applied to problems in cellular signaling and antibody-antigen interactions. I will explore several aspects of our recent research, which has included the development of accurate and efficient numerical simulation methods, the derivation and analysis of rigorous coarse-grained (PDE) limits, and applications to immune signaling. By surveying these different areas, this talk will offer a broad introduction to the mathematical and computational challenges of modeling cellular biology at the single cell scale.
Tue Nov 3, 2026 | 1.30pm | Kemeny 343
Tue Nov 10, 2026 | 1.30pm | Kemeny 343
PDE dynamics on metric graphs and applications
Abstract TBA.
Tue Nov 17, 2026 | 1.30pm | Kemeny 343
Tue Nov 24, 2026 | 1.30pm | Kemeny 343