I am a computational scientist with a focus on understanding molecular systems and communicating that knowledge as clearly as possible. Currently, I am a NESAP for Learning Postdoctoral Fellow at NERSC, leveraging deep learning algorithms to aid in the discovery of catalysis materials in collaboration with Zachary Ulissi. Before starting my postdoc, I finished my PhD in Kristin Persson’s Group, where my research focused on the physics of conducting polymers with the goal of enhanced materials design. Looking forward, the vision for my research is to combine machine learning, high performance computing, and domain expertise to tackle problems in the physical sciences.
Outside of science I enjoy being in the ocean and going on road trips in my van.
PhD in AS&T (Applied Physics), 2019
University of California Berkeley
MS in Microbial Engineering, 2012
University of Minnesota Twin Cities
BS in Biology/Chemistry, 2009
University of Minnesota Duluth
Open source python packages for automating molecular workflows
In macromolecules, there are numerous examples where structure dictates function including electronic conductivity of conjugated polymer materials. To elucidate the structure of doped and excited conjugated polymers we developed a multiscale model that captures electronic structure rearrangement and stochastically generates polymer chain conformations.
Machine learning models for predicting the bulk modulus of materials