معرفی
Ying Jin is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She completed her Ph.D. in Statistics at Stanford University (2019–2024) under the advisement of Emmanuel Candès and Dominik Rothenhäusler, and holds a B.S. in Mathematics and a B.A. in Economics (Finance) from Tsinghua University (2015–2019). Prior to Wharton, she served as a Wojcicki-Troper Postdoctoral Fellow at the Harvard Data Science Initiative, mentored by José Zubizarreta and collaborating with Marinka Zitnik. Her research focuses on causal inference, distribution shifts, and generalization across populations, with contributions to conformal prediction, replication studies, and reinforcement learning. She co-organizes the Online Causal Inference Seminar and has developed open-source tools like the repDiagnosis R package for replication diagnostics.
Research Interests
- Causal inference and effect generalization across populations
- Statistical methodology for distribution shifts and covariate adjustment
- Conformal prediction and post-selection inference
- Machine learning applications in scientific replication and AI safety
Her work bridges theoretical advancements and practical tools, addressing challenges in replicability and uncertainty quantification across disciplines.
