I am a postdoctoral scholar studying theoretical and computational chemistry. This means that theoretically speaking, I can both use computers and do chemistry. I received my PhD working with Profs. Aaron Dinner and Jonathan Weare working on enhanced sampling algorithms. Currently, I am a Research Fellow at the Flatiron Institute, in the Center for Computational Mathematics (CCM).

Current Projects

New approaches for learning on permutation-equivariant structures

Chemical systems are complex arrangements of identical parts. As a consequence, symmetry under permutation is fundamental to chemical systems. However, this symmetry also severely restricts the space of possible learning algorithms: if not constructed carefully, algorithms that attempt to learn new structures must face a combinatorial explosion in the number of terms. Currently, I am working with Prof. Risi Kondor to develop new approaches to learning on chemical systems. By intelligently exploiting and learning substructures of a chemical system, we hope to generate richer representations of chemical systems.

Error Estimates for MBAR

The Multistate Bennett Acceptance Ratio is a popular framework for estimating free energies from multiple biased simulations. In the EMUS paper, we introduced the iterative eigenvector method for umbrella sampling (iEMUS) algorithm that allows for extremely rapid (1-2 iterations) convergence of the MBAR equations. Since then, we have found that iEMUS also allows for accurate error estimates for MBAR that account for autocorrelation times and give state-wise contributions to the error. I am mentoring Sherry Li in exploring and extending these expressions.

Selected Publications