I graduated from Brown University in 2019 with a BSc in Applied Mathematics.
Before Brown, I went to the Hopkins School in New Haven, CT.
Since school, I've been working on Machine Learning methods for drug discovery. I focus
on AutoML, graph-based methods, MLOps, small-data ML (transfer learning), and active learning.
Recently, I've been leading research and development of supervised learning methods at Schrodinger,
with particular emphasis on supporting medicinal chemistry operations. A large part of that work is
dedicated to the development and productization of an automated molecular property prediction application.
I believe that easy-to-use and predictive machine learning models are very powerful tools. These models are
able to augment and extend the capabilities of domain experts, allowing for data-driven decision making.
Drug discovery, materials science, and computational chemistry software. Take a look!
Schrodinger's Website