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Sang Michael Xie Image could not be loaded.

I am a Computer Science Ph.D. student studying Machine Learning at Stanford University, advised by Percy Liang and Tengyu Ma.
I am grateful to be supported by the NDSEG Fellowship starting Fall 2019. Previously, I received my B.S. with departmental honors and M.S. in Computer Science from Stanford in 2017, where I am grateful to have worked with Stefano Ermon on machine learning methods for sustainability, particularly in poverty mapping using satellite imagery.

My research interests are in generalization to unseen inputs and tasks (robustness to distribution shifts, extrapolation), learning with unlabeled data and limited supervision (transfer learning, semi-supervised learning, unsupervised learning), and applications/motivations from real-world robustness problems and NLP.

[Music] Email: xie AT





I have reviewed for NeurIPS (2019, 2020, 2021, 2022), ICML (2020, 2022), ICLR (2021, 2022), ICML 2022 First Workshop on Pre-Training, ICML 2022 Principles of Distribution Shift (PODS) workshop, the NeurIPS 2021 Workshop on Distribution Shifts (DistShift), the Workshop on Computer Vision for Global Challenges (CV4GC) at CVPR 2019.