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I work at Google Brain doing research on machine learning interpretability, model compression and security.
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 and instead depend upon a community of 100+ data volunteers. I taught myself machine learning and hope to help others do the same. 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. In 2017, I collaborated with 8 other data professionals to start a program called Delta Teaching Fellows. We taught an introduction to machine learning course in Kenya and our fellows will teach this year in Morocco. You can learn about our mission here. Learn how you can join our team as a Delta teaching fellow in 2018/19 here. I grew up in Southern Africa, in South Africa, Mozambique, Swaziland, Lesotho and Kenya. My family now lives in Monrovia, Liberia. |
Recent updates:
I was mentioned in an article by the Economist on recent efforts to demystify deep learning and equip the masses to use the technology. I felt uneasy about the tone of the article, and wrote a blog post about the danger of creating the impression that there is a quick Cinderella story for anyone who wants to teach themselves machine learning.
Our field needs more diversity. There must be more people like me who feel welcome and who are given the tools to succeed. However, part of preparing people to succeed is to be candid about how challenging it can be and how many failures there are along the way. I am concerned that the story in the Economist not only displaces my own narrative but also sets unrealistic expectations for anyone setting out in the field. Read my full thoughts here.
Our field needs more diversity. There must be more people like me who feel welcome and who are given the tools to succeed. However, part of preparing people to succeed is to be candid about how challenging it can be and how many failures there are along the way. I am concerned that the story in the Economist not only displaces my own narrative but also sets unrealistic expectations for anyone setting out in the field. Read my full thoughts here.
TWIML podcast: I really enjoyed this chat with Sam Carrington about the nuances of interpretability, how do we evaluate the reliability of the methods we use to interpret deep learning models, and of course my own journey as a researcher.
