🎓 04/2021: I am grateful to accept an NSF Graduate Research Fellowship in support of my PhD research.
I’m interested in multimodal learning problems at the intersection of computation and cognition.
I completed my undergrad at Harvard SEAS in 2018, where I concentrated in Computer Science and Mind, Brain & Behavior. I wrote my undergraduate thesis and two publications on Visual Question Answering under Alexander Rush and Stuart Shieber and in close collaboration with Yonatan Belinkov. I’ve completed two research-focused internships at Google Brain (now Google AI) on Google’s Tensorflow and OCR teams. Previously, I was an RA in the Fedorenko Lab at MIT, where I worked on computational models of lexical semantics.
I’m currently a research engineer at Reverie Labs, where I’m working to apply techniques from computational chemistry, natural language processing, and computer vision to discover novel molecules for treating brain cancer. My work employs graph-based deep learning (neural message passing, graph convolutions, etc.) and large-scale language models (e.g., transformers) for molecular property prediction. I’ve also worked on molecular generation, docking, virtual screening design, interfaces for human-machine interaction, and have contributed to the open science COVID Moonshot effort. I’ve written about some of these projects in the Reverie Blog.
I like to read broadly and keep up with the literature. One of the areas I’m most excited about is methods that combine neural architectures with symbolic reasoning. Some of my current areas of interest are:
- Neuro-symbolic learning models
- Probabilistic programming
- Program synthesis
- ML interpretability and bias
- Model generalization and out-of-distribution (OOD) testing
- Few-shot learning
- Bayesian ML
Outside research, I enjoy playing and composing music. I’m a trained jazz guitarist and amateur keyboard player. I’ve recently been enjoying learning the Seaboard, which is a 5D MPE instrument that is mind-bending to play.Last updated on May 19, 2021