CV
Education
- Ph.D in Chemical and Biomolecular Engineering, UC Berkeley, 2023
- B.S. in Chemical and Biological Engineering, Northwestern University, 2018
Work experience
- (Sept. 2023 - present) MIT Lincoln Laboratory: Technical Staff
- Leading the search for rare-earth-free permanent magnets via high throughput density functional theory (DFT) calculations, spin-informed machine-learned interatomic potentials (MLIPs), and Monte Carlo simulations for finite-temperature magnetic properties
- Accelerated DFT calculations by 26% using predicted charge density grids from a rotation-equivariant machine learning model
- Led the implementation of generative models, active learning, and reaction prediction strategies to discover novel molecular colorimetric sensors, reducing experiment time
- Developed proof-of-concept model for public health monitoring via antibody binder identification using AbLang, ESM-2
- Led informal presentations and discussions on how machine learning and computational chemistry can accelerate materials discovery for defense applications
- Presented work at APS, ACS, RAAINS
- (Summer 2019) Kebotix: Machine Learning Intern
- Created graph convolutional neural networks for predicting optoelectronic properties of small molecules. Discovered novel candidates not studied in the academic or patent literature.
- Created web interface for receiving chemist feedback in a self-driving laboratory prototype
Academic experience
- (2018 - 2023) UC Berkeley, Neaton Group: PhD candidate
- Developed novel artificial intelligence methodology for efficient materials discovery
- Consistently found top methane storage materials by evaluating only ~0.1% of the database using Bayesian optimization
- Discovered new air separation materials with a novel acquisition function to target O2 adsorption enthalpy
- Determined trends in air separation materials using quantum chemistry calculations and benchmarked density functional approximations
- Theorized and experimentally verified new, high adsorption energy carbon capture material using machine-learned interatomic potentials and simulated annealing in LAMMPS
- Presented findings at multiple conferences such as APS, ACS, AIChE
Awards
- (2023) Kavli ENSI Philomathia Graduate Fellowship
- (2019) NSF Graduate Student Research Fellowship Honorable Mention
- (2019, 2021) 2x Outstanding Graduate Student Instructor Award
