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.

I will be a Wojcicki-Troper Postdoctoral Fellow at Harvard Data Science Insititute during 2024-2025. Starting July 2025, I will be an assistant professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania.

I currently help organize the Online Causal Inference Seminar.

Research interests

I work on statistical methods and applications related to (i) confident deployment of black-box prediction models in critical domains, and (ii) generalization of statistical findings to new contexts. Specifically,

• Uncertainty quantification: quantifying confidence and limiting mistakes in black-box prediction models; applications in biomedical discovery and AI-powered decisions.

• Generalizability and replicability: understanding and addressing realistic distribution shifts in generalizing treatment effects, replicating experiments, and learning new decision rules.

News

• March 2024: How to quantify the uncertainty for an “interesting” unit picked by a complicated, data-driven process? Check out JOMI, our framework for conformal prediction with selection conditional coverage!

• Jan 2024: Interested in making good decisions with conformal prediction? I’ll give a virtual talk “Selecting Trusted Decisions from AI Black Boxes: Correcting Conformal Prediction for Selective Guarantees” at MLBoost (event link, Jan 4, 2024). This is based on our work on model-free selective inference [1, 2].

• Oct-Dec 2023: I’m presenting at INFORMS Annual Meeting, WORDS 2023 at Duke University, Rising Stars in Data Science Workshop at UChicago, and “Causality, generalizability, and robustness” session at ICSDS 2023.

• Nov 2023: Do you want to leverage auxiliary/unsupervised data to improve your (high-dimensional) prediction models? Check out our paper for our Modular Regression method, accepted at JMLR!

• Sept 2023: Our paper on graph conformal prediction is accepted at NeurIPS 2023 as a spotlight!

• 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.

• 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.

• 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.

• 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!