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Research scientist at Google Brain working on training models that go beyond test-set accuracy to fulfill multiple desired criteria -- interpretable, compact, fair and robust. I enjoy working on research problems where progress translates to reliable and accessible machine learning in the real-world.
I founded a local Bay Area non-profit called Delta Analytics that works with non-profits and communities all over the world to build technical capacity and empower others to use data. I am one of the co-founders of the Trustworthy ML Initiative, a forum and seminar series related to Trustworthy ML. I believe in open research and collaborating widely. I am on the advisory board of Patterns, and amongst other efforts, I am an active member of the MLC research group which has a focus on making participation in machine learning research more accessible. If you made it this far -- Listen to underrated ml -- a podcast where Sean Hooker and I discuss underrated ideas in machine learning. Other writing: The Hardware Lottery Why data for good lacks precision. Research Papers |
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