Neal Jean

I'm a PhD student working with Stefano Ermon in the Stanford AI Lab. My research tackles challenging sustainability problems using machine learning and artificial intelligence.

I previously studied econ and math at Duke and electrical engineering at Georgia Tech. I love basketball—my senior thesis was about the NBA—but unfortunately I'm too deep in this school thing now to make it to the league.

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Right now I'm interested in computational sustainability, semi-supervised learning, and unsupervised learning. There's a lot of unlabeled data out there—we need to figure out how to use it!

Semi-supervised deep kernel learning
Neal Jean, Michael Xie, Stefano Ermon
NIPS Bayesian Deep Learning Workshop, 2016  
PDF / Poster

Adapted Deep Kernel Learning models for semi-supervised learning.

Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, Stefano Ermon
Science, 353(6301), 790-794, 2016  
Video / PDF / Website / Commentary / Code

Produced district-level poverty maps in Nigeria, Tanzania, Uganda, Malawi, and Rwanda based on household consumption and asset-based wealth measures. Bill Gates tweeted our research!

Transfer learning from deep features for remote sensing and poverty mapping
Michael Xie, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon
AAAI Conference on Artificial Intelligence, 2016  
PDF / NY Times

Extracted features from high-resolution satellite images with convolutional neural networks to estimate poverty in Uganda.