About Me

I am a Research Engineer at Fundamental AI Research (FAIR) at Meta working on deep learning applications for chemistry. As part of the Open Catalyst Project, I help develop datasets, models, and frameworks to help address societal energy and environmental challenges, particularly climate change. My current focus is on electrocatalyst discovery for applications like CO2 reduction and oxygen evolution for renewable energy storage. I spend a lot of time building out and running large scale inference campaigns that make the most of our AI advancements. More importantly, I collaborate with experimental partners to help make our AI discoveries a reality in the lab.

Prior to this, I completed my PhD in Chemical Engineering (2022) at Carnegie Mellon University (Thesis), advised by Zachary Ulissi. Prior to grad school, I worked for the U.S. Environmental Protection Agency as an Environmental Engineer in the Air & Radiation Division. There I was involved in particulate matter emissions modeling, field inspections, and settlement negotiations. I graduated from Illinois Tech in 2017 with a Bachelor’s and Master’s of Advanced Studies in Chemical Engineering.

In my free time I enjoy spending time with family and friends. I always enjoy a good competition when it comes to sports and board games – my favorites being volleyball and Catan. My wife and I love exploring cities, parks, and new food places.