In Fall 2026 I am teaching Math 417 (multivariate analysis) and Math 345 (differential equations).

My current research focuses on the analysis, design, and optimization of numerical methods for applications in computational chemistry, using tools from applied probability, linear algebra, uncertainty quantification, and machine learning.

Over the last decade, scientists have poured enormous effort into predicting structure: for example, how a protein is expected to fold, or how a drug might bind with a protein. These efforts have led to breakthroughs such as AlphaFold, which has streamlined parts of structural biology and the initial stages of drug discovery. A much smaller subset of researchers are working toward understanding dynamics, especially residence times, or how long a drug typically binds with a protein before breaking free. These problems are very difficult, and although progress has been slow and methods are not yet routinely scalable, this work is important: residence time can have critical impacts on drug efficacy, safety, and tolerability that cannot be determined from structure alone. Efficiently and reliably estimating residence times has been a theme of several of my recent collaborations.

Some of my latest articles (supported by the NSF) are listed below. A complete list of my publications is here.