I am a 1st year Computer Science Ph.D. student studying Machine Learning at Stanford University. Currently, I am doing rotations with Tengyu Ma, Stefano Ermon, and Percy Liang. Previously, I received my B.S., M.S. in Computer Science 2017 at Stanford University, specializing in Artificial Intelligence and Theory.
Extrapolation, robustness, and uncertainty
Encoding invariances and inductive biases with structure, end-to-end training of structured models, interpretability with structure
Transfer learning, domain adaptation, semi-supervised learning, unsupervised learning, meta-learning
Reparameterizable Subset Sampling via Continuous Relaxations, 2019. [Paper]Sang Michael Xie, Stefano Ermon
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. Neural Information Processing Systems (NeurIPS), 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
Semi-supervised Deep Kernel Learning. Amazon Graduate Symposium, 2019.
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Association for the Advancement of Artificial Intelligence (AAAI), 2016.