I am an AI researcher at Meta FAIR working on applying deep learning to scientific problems.
My focus over the last few years has been on using AI to accelerate material discovery for combating climate change, specifically renewable energy storage and carbon capture. As part of the Open Catalyst Project (OCP), I have been deeply involved in discovering new electrocatalysts crucial for renewable energy storage. This work addresses the intermittent nature of renewable energy sources like wind and solar by exploring scalable solutions for storing excess energy as fuels. I have also led the OpenDAC project, focusing on discovering new sorbents for Direct Air Capture (DAC) of carbon dioxide from ambient air. Our goal is to lower the cost of carbon dioxide capture, a critical step for achieving global net zero goals.
Previously, I worked on large scale self-supervised and multilingual speech recognition as the manager for the FAIR Speech team, scaling speech recognition systems to unprecedented scales in terms of model size, data size and number of languages.
I have also worked on developing AI models to accelerate MRI scans by 4x and validated them clinically as part of the fasMRI project. Accelerating MRIs can reduce costs, minimize stress to patients and make MRIs more accessible.
Prior to joining Meta, I worked as a Senior Research Scientist at Baidu Silicon Valley AI Lab with Andrew Ng on speech recognition, where I was part of the team that developed the Deep Speech 2 model. Prior to Baidu, I worked as an AI Engineer at Twitter, building online ML pipelines for ad ranking and targeting.
I have a Masters in Language Technologies from the Language Technologies Institute at Carnegie Mellon University, where I worked with Prof. Roni Rosenfeld on modeling the spread of infectious diseases using machine learning and agent based modeling as part of the FRED project.