Welcome!

I am an Artificial Intelligence researcher at Meta FAIR, passionate about leveraging AI to combat climate change. I lead the OpenDAC project, focused on discovering innovative materials for Direct Air Capture (DAC) to remove carbon dioxide from the atmosphere. Our aim is to make DAC more affordable and scalable, critical for achieving net-zero emissions.

I am also deeply involved in the Open Catalyst Project (OCP), working to discover novel electrocatalysts for renewable energy storage. These catalysts will enable us to overcome the intermittent nature of solar and wind power by converting excess energy into storable fuels.

Previously, I led the FAIR Speech Recognition team in developing large-scale, self-supervised, and multilingual speech recognition systems. This involved pushing the boundaries of model size, dataset size, and the number of supported languages.

As part of the fastMRI project, I led research that resulted in a four-fold acceleration of MRI scans. These models are now used in clinical practice for most new MRI scans at NYU.

From 2016 to 2018, I worked at Baidu Silicon Valley AI Lab on speech recognition, contributing to the development of the Deep Speech 2 model, that was named a top-10 technological breakthrough of 2016 by MIT Tech Review. Prior to that, I built online machine learning models for ad targeting at Twitter.

I hold a Master’s in Language Technologies from Carnegie Mellon University’s Language Technologies Institute, where I worked with Prof. Roni Rosenfeld on applying machine learning and agent-based modeling to study the spread of infectious diseases as part of the FRED project.

My research has been featured in prominent publications such as the Wall Street Journal, CNBC, USA Today, Reuters, Fortune, TechCrunch, and MIT Tech Review.