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I am a Director at Cohere and I lead Cohere For AI, a non-profit research lab that seeks to solve complex machine learning problems. We support fundamental research that explores the unknown, and are focused on creating more points of entry into machine learning research. I lead a team of researchers and engineers working on making large language models more efficient, safe and grounded.
Prior to Cohere, I was a research scientist Google Brain doing work 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 remain an advisor on the Delta Analytics board. 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 on Kaggle's ML Advisory Research Board. 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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