I am a researcher at Google Brain working on training models that go beyond test-set accuracy to fulfill multiple desired criteria -- interpretable, compact, fair and robust.
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​Before Brain, 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. All our initiatives are run without any full time staff.

This website allows me to share some of my current research and resources from talks and suggests ways to get involved if you want to help build technical capacity around the world.

I am an active member of the MLC research group, which has a focus on making participation in machine learning research more accessible.

I am one of the organizers of the Trustworthy ML Initiative, a forum and seminar series related to Trustworthy ML. 

I believe in open research, and make time to collaborate with independent researchers around the world. If you would like to collaborate, reach out with some context about your research interests.

I grew up in Africa, in South Africa, Mozambique, Swaziland, Lesotho and Kenya. My family now lives in Monrovia, Liberia.

Learn more about my journey into machine learning -- Slow Learning.

Listen to underrated ml -- a family hobby podcast where Sean Hooker and I discuss ideas in machines learning are underrated.

Other writing:

The Hardware Lottery

Why data for good lacks precision.

Research Papers

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