I am a PhD student in theoretical chemistry at Yale developing new methods for materials simulation and rational design. Currently, I'm funded by an NDSEG fellowship through the Office of Naval Research. Before grad school, I did my undergrad at Princeton, where I studied biocatalysis for my senior thesis.
In my spare time, I like to play the trumpet and spend time in nature.
I’m broadly interested in developing physics-informed and data-driven methods for first principles simulations. By working between quantum chemistry, artificial intellgience, and materials discovery, I aim to make materials modeling more robust, scalable, and insightful, across the periodic table. Beyond fundamental method development, I'm applying techniques - both old and new - to address challenges in spectroscopy, functional materials, and catalysis.
Low-cost orbital descriptors enable DFT Hamiltonian learning with high accuracy and efficiency. Downfolding large basis sets enables distillation of the low energy spectrum. Applications to QM9 molecules and tight binding models of intermolecular charge transfer are presented.
We present a molecular extension of a recently proposed Green’s function embedding method, interacting-bath dynamical embedding theory (ibDET), for computing charged excitation energies at the GW and EOM-CCSD levels.
A deep learning framework that unifies ground and excited state property prediction by targeting the many-body Green’s function (MBGF) and self-energy. I design and implement a graph neural network called MBGF-Net that learns the underlying physics to derive properties with competitive accuracy, unprecedented data efficiency, and impressive transferability and generalizability.
