About Me
I am a postdoctoral scholar studying theoretical and computational chemistry. This means that, theoretically, 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 CCM.
Current Projects

New approaches for learning on Permutationequivariant 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. 
Neural Architectures for CryoEM
CryoEM allows one to capture millions of images of a biomolecule, each of which is a twodimensional projection of the molecular structure. These images must then be combined to form a threedimensional molecular structure; a task complicated by the high noise levels in the image. A key step in the recombination is learning the relative angles between the projection axis in each image. Hannah Lawrence and I are developing neural techniques for learning these relative rotations. In addition, I am working with Dr. Pilar Cossio on new algorithms for extracting free energy surfaces from CryoEM data. 
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 (12 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 statewise contributions to the error. I am mentoring Sherry Li in exploring and extending these expressions.
Selected Publications
 Integrated Variational Approach to Conformational Dynamics: A Robust Strategy for Identifying Eigenfunctions of Dynamical Operators
 Error bounds for dynamical spectral estimation (Accepted at SIAM Journal for Data Science)
 Galerkin approximation of dynamical quantities using trajectory data
 Stratification as a general variance reduction method for Markov chain Monte Carlo
 Eigenvector method for umbrella sampling enables error analysis