Why you may predict distribution shifts in generalization, and how
About me
Hi! I’m Ying. I am 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.
I obtained my PhD in Statistics from Stanford University in 2024, advised by Professors Emmanuel Candès and Dominik Rothenhäusler. Prior to that, I studied Mathematics at Tsinghua University. Before joining Wharton, I spent one year as a Wojcicki-Troper Postdoctoral Fellow at Harvard Data Science Initiative, where I had the fortune to work with Professor José Zubizarreta and Professor Marinka Zitnik.
Research interests
Modern AI systems act as imperfect proxies: they select cases to prioritize, label data at scale, or generate hypotheses that humans act on. I study the statistical foundations for reliable AI use in high-stakes domains, around three connected themes:
- Uncertainty quantification and quality control for AI models: Designing procedures that decide when AI predictions should (not) be trusted under explicit budgets of error, with recent focus on conformal inference with multiple unlabeled data and data-driven decisions. Applications include drug discovery, safe deployment of medical AI, and generative design.
- Generalizability, causality and robustness: Studying the generalization and robustness of statistical findings across datasets, populations, and contexts. Topics include distribution shifts in large-scale replication studies, and methods in transferring causal effects, policy learning, robust balancing weights, sensitivity analysis, and data integration.
- Agentic scientific discovery: Developing the statistical foundations for AI systems that generate and prioritize scientific hypotheses from multi-source, large-scale data, aiming to build agents that are both creative and statistically credible. See POPPER.
Motivated by biomedical collaborations, my work builds general methods in conformal inference, selective inference, and causal inference.
News
- Feb 2025: New papers on conformal prediction with uniform validity across multi-distributions and online selective conformal prediction for arbitrary selection rules via permutation test! New column for “Catalytic Causal Conversations” at Harvard Data Science Review.
- Nov 2025: We develop Cross-Balancing for constructing weights in observational studies with data-driven balancing features, via fitted models or selected variables, combined with expert knowledge. It offers efficient estimation, valid inference, and reduced bias!
- Sep 2025: Our paper on the predictive role of covariate shift in generalizability is accepted to PNAS! Analyzing multi-site replication projects, we find covariate shift does not explain away site differences, but informs the unknown conditional shift. See my blog post!
- Feb 2025: Imagine AI agents for scientific discovery—that gather knowledge by creative reasoning and tool use. How to ensure the soundness of what they acquire? We propose POPPER, where AI agents automate hypothesis validation with error control!
Other recent activities
- Sep 2025: We are organizing a NeurIPS 2025 workshop on Causality in Science (CauScien)!
- Sep 2025: Pessimistic Policy Learning is selected by Annals of Statistics to present at NeurIPS 2025 journal-to-conference track!
- June 2025: I’m giving a talk on optimal variance reduction in A/B testing at the ASA CPID webinar on June 3! [Register here]
- May 2025: I’m organizing an invited session on generalizability, transportability, and distribution shift at ACIC 2025!
- Apr 2025: I gave a talk on our POPPER agent framework at the International Seminar on Selective Inference! [slides] [recording]
- Nov 2024: Excited to share Optimized Conformal Selection for optimizing/selecting conformity scores to pick as many “interesting” instances as possible (e.g., active drugs, trustable LLM outputs) while maintaining FDR control – even without sample splitting!
- Sept 2024: Outputs from black-box foundation models must align with human values before use. For example, can we ensure only human-quality AI-generated medical reports are deferred to doctors? Our paper Conformal Alignment is accepted to NeurIPS 2024!
- Sept 2024: I’m thrilled to receive the 2025 IMS Lawrence D. Brown PhD student award!
- Sept 2024: My paper on optimal variance reduction in online experiments (2021 internship project at LinkedIn) receives the 2024 Jack Youden Prize for the best expository paper in Technometrics! Thank you, ASQ/ASA!
- 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!
- Sept 2023: I’ll be giving a seminar at Genentech on leveraging Conformal Selection [1, 2] for reliable AI-assisted drug discovery.
- Sept 2023: Scientists often refer to distribution shifts when effects from two studies differ, e.g. in replicability failure. Do they really contribute? See our preprint for a formal diagnosis framework. Play with our live app, or explore our data repository! I gave an invited talk about it in the Causality in Practice Conference.
Beyond academics, I love traveling and photography in my free time. See my photography gallery!
Education
- Ph.D, Statistics, Stanford University, 2019 - 2024.
- B.S. Mathematics, Tsinghua University, 2015 - 2019.
- B.A. Ecomonics (Finance), Tsinghua University, 2015 - 2019.