AI

Letter from the Chair, Spring 2026

In just a few days I will hand the reins of the Department of Mathematics to the experienced hands of my colleague Joel Hass. Joel has served as department chair for 4 years (2010-2014). Through a number of leadership functions on campus he also acquired deep knowledge of all aspects of the university organization. I am very grateful he agreed to step back into the department chair role for 2026-27. 

GGAM Update, 2026

This is my first year as GGAM Chair and the first time I have the honor of writing this column. I took over from Javier Arsuaga last summer. Javier did an incredible job moving GGAM forward—many thanks to him—and I am grateful to be able to build on and continue that work.

Undergraduate Program, 2026

It has been another busy year for our undergraduate mathematics programs, with many exciting changes on the horizon. This year, our Undergraduate Program Committee is leading teams of faculty members in a review of all four of our majors – Mathematics, Applied Mathematics, Analytics and Operations Research, and Scientific Computation – with a view towards modernizing and improving what our current programs offer. The intent is to both add breadth to our current course offering, and to add support to mathematics students in the form of discussions across all of our classes.

Branching Out

With the anticipation of Data Science activities in full swing at UC Davis, in Fall 2020, our campus decided to renovate the first floor and basement of the former Physical Sciences and Engineering Library (PSEL) for the use of campus-wide Data Science and Artificial Intelligence (AI) related activities. The renovation was finally completed in December 2023.

Joining Us: Shizhou Xu

Shizhou Xu, originally from China, earned his Ph.D. from the University of California, Davis in 2024 under the supervision of Professor Thomas Strohmer. His research focuses on the intersection of mathematics and trustworthy artificial intelligence, with particular emphasis on fairness, privacy, interpretability, and robustness in machine learning.