Hi! I am a final-year PhD student at Department of Statistics, Stanford University. I am fortunate to be advised by Professors Emmanuel Candès and Dominik Rothenhäusler. Here are my CV, Github and Google scholar pages.
My research centers around uncertainty and robustness. I design statistical methods that quantify the uncertainty of prediction models for their deployment in real decisions. Additionally, I work on addressing distribution shifts – of various forms – that arise in predictive inference, causal inference, replicability, and offline policy learning, with a recent focus on their empirical grounds.
I currently help organize the Online Causal Inference Seminar.
I’m on the 2023-2024 job market!
News
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Oct-Dec 2023: I’m presenting at INFORMS Annual Meeting, WORDS 2023 at Duke University, and the “Causality, generalizability, and robustness” session at ICSDS 2023.
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Sept 2023: Our paper on graph conformal prediction is accepted at NeurIPS 2023 as a spotlight!
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Sept 2023: I’ll be giving a seminar at Genentech Inc. on how to leverage model-free selective inference and conformal inference for reliable AI-assisted drug discovery.
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Sept 2023: Our preprint on diagnosing replicability failure is out. Play with our live diagnosis app, or explore our data repository! I gave an invited talk about it in the Causality in Practice Conference in June.
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July 2023: Our preprint on model-free selective inference is out! It develops new multiple testing techniques to deal with distribution shifts. I talked about this series of work [1, 2] at ICSA, JCSDS, and JSM.
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June 2023: My paper on the Natarajan dimensions of popular function classes is accepted to ISIT 2023!
Education
- Ph.D, Statistics, Stanford University, 2019 - Now.
- B.S. Mathematics, Tsinghua University, 2015 - 2019.
- B.A. Ecomonics (Finance), Tsinghua University, 2015 - 2019.
Beyond academics, I love traveling and photography in my free time. See my photography gallery!