Wojcicki-Troper Postdoc Fellow
@ HDSI & HMS

Contact
yjin at hcp.med.harvard.edu
180A Longwood Ave, Boston, MA

About me

Hi! I’m Ying. I am currently a Wojcicki-Troper Postdoctoral Fellow at Harvard Data Science Initiative, where I have the fortune to be mentored by Professor José Zubizarreta and also work with Professor Marinka Zitnik.

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 obtained my PhD in Statistics from Stanford University in 2024, advised by Professors Emmanuel Candès and Dominik Rothenhäusler. Here are my CV, Github and Google scholar pages.

I currently help organize the Online Causal Inference Seminar.

Research interests

I work on statistical problems related to two main themes:

  • Uncertainty quantification
    I develop methods to quantify and control the uncertainty of black-box AI models for their confident deployment in critical domains. Specifically, I think about what guarantees are meaningful, how to achieve them with easy-to-use, widely-applicable methods, and their applications in biomedical discovery and human decisions.

  • Generalizability and robustness
    I am interested in understanding the generalization and robustness of statistical findings across datasets, populations, and contexts. Specifically, I study the (empirical) nature of real distribution shifts, models to describe them, and methods to protect against or adapt to them in generalizing treatment effects, replicating causal experiments, and learning decision rules.

These questions lead me to the fields of conformal prediction, causal inference, and multiple testing.

News

  • Dec 2024: Recent empirical investigations have challenged the sufficiency of covariate shift adjustment for generalization under distribution shift. How to address what remained unexplained? Analyzing two large-scale multi-site replication projects, our new paper suggests a predictive role of covariate shift: it informs the strength of unknown conditional shift, which helps generalization!

  • Nov 2024: Excited to share new work on Optimized Conformal Selection with amazing collaborator Tian Bai (undergrad at McGill). We show how to optimize/select conformity scores in Conformal Selection to pick out 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: 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