In Spring 2026 I am teaching Math 435 and Math 161.

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

Over the last decade, scientists have poured enormous efforts into predicting structure: for example, how a protein is expected to fold, or how a drug might bind with a protein. These efforts have lead to the huge successes of AlphaFold in streamlining the initial stages of drug design (where target proteins are selected), saving potentially years of work on many projects. A much smaller subset of researchers are working toward understanding dynamics, and 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 results are not yet scalable, this work is important: the residence time has critical impacts on drug safety and tolerability that cannot be determined from structure alone.

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