معرفی
Ying Jin is an Assistant Professor at the Department of Statistics and Data Science, Wharton School, University of Pennsylvania. She holds a PhD in Statistics from Stanford University (2024), advised by Emmanuel Candès and Dominik Rothenhäusler, and undergraduate degrees in Mathematics and Economics from Tsinghua University (2019). Her research focuses on uncertainty quantification in AI models, causal inference, conformal prediction, and addressing distribution shifts in scientific replication studies.
- Education:
- PhD, Statistics, Stanford University (2019-2024)
- BS, Mathematics, Tsinghua University (2015-2019)
- BA, Economics (Finance), Tsinghua University (2015-2019)
Her recent work develops frameworks like POP for hypothesis validation with LLM agents and Conformal Alignment for trustworthy foundation models. She has published in top venues (NeurIPS, ICML, PNAS) and received awards including the IMS Lawrence D. Brown PhD Student Award (2025) and Jack Youden Prize (2024). Her teaching includes STAT4710/5710 - Modern Data Mining, covering regression, tree-based methods, and deep learning with R programming.
Research interests span Uncertainty Quantification in AI for high-stakes domains like drug discovery, and Generalizability across datasets through covariate shift analysis and causal decision rules. She organizes the Online Causal Inference Seminar and co-developed the repDiagnosis R package for replication studies.
