About Me

I am a Ph.D. Candidate in Chemical Engineering and Computation at MIT doing research in molecular design and discovery with Rafael Gómez-Bombarelli and Bill Green. My current research interests are in using the combination of physics-based simulations and machine learning with experimental collaboration for the discovery and design of new molecules and materials. This motivates my current work on several projects that integrate representation learning, uncertainty estimation, active learning, and generative modeling with results from time-dependent density functional theory (TD-DFT) calculations. I am applying these methods for the design of molecules based on their optical properties. During my Ph.D., I have also had the opportunity to intern with Eli Lilly and Microsoft Research.

During my undergraduate studies, I studied the structural, thermodynamic, and optical properties of nitride semiconductors, and I developed a nanoHUB.org tool for research and education on computational catalysis.

I am also very interested in teaching and mentorship. In my senior year at the University of Michigan, I led the development of the computational curriculum for a new undergraduate chemical engineering class. My team designed the class to introduce underclassmen to the breadth of research opportunities in chemical engineering and to give hands-on opportunities to explore some of these areas and practice common research techniques. I created and maintain the Awesome Chemical Engineering Education repository on GitHub, which is a curated list of online chemical engineering education resources, with an emphasis on materials that are free and open source and that include a significant computational component (e.g., Python, MATLAB, etc.).