I am a Computer Science Ph.D. student studying Machine Learning at Stanford University. Previously, I received my B.S.H., M.S. in Computer Science 2017 at Stanford University, specializing in Artificial Intelligence and Theory. My interests include making AI accessible to all potential application fields, increasing the interpretability of machine learning models, and increasing the robustness of AI models. My subject interests revolve around how theory from statistics, optimization, algorithms, and other math can be applied to machine learning. In my undergraduate career, I conducted research under Prof. Stefano Ermon in the Stanford Artificial Intelligence Laboratory, working on machine learning with limited data, including transfer learning and spatiotemporal machine learning models for semi-supervised learning, applied to predicting poverty measures in Africa from satellite imagery. I also helped to start the Sustainability and AI (SUSTAIN) lab at Stanford.
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. Neural Information Processing Systems (NIPS), 2018. [Paper]
Neal Jean*, Sang Michael Xie*, Stefano Ermon
Neal Jean*, Marshall Burke*, Michael Xie, William Davis, David Lobell, Stefano Ermon
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Association for the Advancement of Artificial Intelligence (AAAI), 2016. Oral Presentation, NVIDIA Global Impact Award Finalist, Scientific American 10 World Changing Ideas of 2016 [Paper] [Oral Presentation] [Stanford Report] [NYTimes]
Michael Xie, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon
Mapping Poverty with Satellite Imagery. Honors Thesis for B.S. with Honors [Paper]
Semi-supervised Deep Kernel Learning. Neural Information Processing Systems (NIPS) Bayesian Deep Learning Workshop, 2016. [Paper]
Neal Jean, Michael Xie, Stefano Ermon
Incorporating Spatial Context and Fine-grained Detail from Satellite Imagery to Predict Poverty [Paper]
Jae Hyun Kim, Michael Xie, Neal Jean, Stefano Ermon
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Association for the Advancement of Artificial Intelligence (AAAI), 2016.