I am a PhD student in theoretical chemistry at Yale, where I am advised by Tianyu Zhu. I am an NDSEG fellow working for the Office of Naval Research to develop new methods for predicting excited-state properties of materials.
Before starting my PhD, I did my undergrad at Princeton, where I worked with Greg Scholes for my senior thesis.
I’m interested in quantum chemistry, materials science, and artificial intelligence, with a focus on developing efficient and accurate methods for predicting many-electron properties beyond standard DFT. My work aims to bring together modern machine learning approaches with traditional physical chemistry to address challenges in spectroscopy, materials science, and catalysis.
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.
