Calendar

Posted November 15, 2025
Last modified January 21, 2026

Algebra and Number Theory Seminar Questions or comments?

Olivia Beckwith, Tulane University
Polyharmonic Maass forms and Hecke series for real quadratic fields

We study polyharmonic Maass forms and show that they are related to ray class extensions of real quadratic fields. In particular, we generalize work of Lagarias and Rhoades to give a basis for the space of polyharmonic Maass forms for $\Gamma(N)$. Modifying an argument of Hecke, we show that twisted traces of cycle integrals of certain depth 2 polyharmonic Maass forms are leading coefficients of Hecke $L$-series of real quadratic fields. This is ongoing joint work with Gene Kopp.

Event contact: Gene Kopp

Posted January 22, 2026

Informal Analysis Seminar Questions or comments?

Han Nguyen, LSU
Introduction to Finite Element Methods

This talk serves as an exposition of ongoing work in Finite Element Methods.

Posted January 15, 2026

Informal Geometry and Topology Seminar Questions or comments?

To Be Filled In

Posted November 22, 2025
Last modified January 6, 2026

Control and Optimization Seminar Questions or comments?

Henk van Waarde, University of Groningen IEEE L-CSS Outstanding Paper and SIAM SIAG/CST Prize Awardee
Data-Driven Stabilization using Prior Knowledge on Stabilizability and Controllability

Direct approaches to data-driven control design map raw data directly into control policies, thereby avoiding the intermediate step of system identification. Such direct methods are beneficial in situations where system modelling is computationally expensive or even impossible due to a lack of rich data. We begin the talk by reviewing existing methods for direct data-driven stabilization. Thereafter, we discuss the inclusion of prior knowledge that, in conjunction with the data, can be used to improve the sample efficiency of data-driven methods. In particular, we study prior knowledge of stabilizability and controllability of the underlying system. In the case of controllability, we prove that the conditions on the data required for stabilization are equivalent to those without the inclusion of prior knowledge. However, in the case of stabilizability as prior knowledge, we show that the conditions on the data are, in general, weaker. We close the talk by discussing experiment design methods. These methods construct suitable inputs for the unknown system, in such a way that the resulting data contain enough information for data-driven stabilization (taking into account the prior knowledge).