معرفی
Ying Jin is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She received her PhD in Statistics from Stanford University in 2024, advised by Emmanuel Candès and Dominik Rothenhäusler, and holds a B.S. in Mathematics and B.A. in Economics from Tsinghua University.
- Current faculty at UPenn Wharton
- Former postdoctoral fellow at Harvard Data Science Initiative
Her research focuses on Uncertainty Quantification and Generalizability in AI models, particularly through conformal prediction, causal inference, and multiple testing. Recent work explores distribution shifts in large-scale replication studies and methods for trustworthy AI in drug discovery and medical applications.
Key article trends show expertise in:
- Conformal prediction methods
- Causal inference under distribution shifts
- LLM-driven scientific discovery
- Replicability analysis
- AI uncertainty quantification
- High-stakes AI validation
Scientific awards include:
- 2025 IMS Lawrence D. Brown PhD Student Award
- 2024 Jack Youden Prize for best expository paper in Technometrics
She organizes the Online Causal Inference Seminar and contributes to open science through the awesome-replicability-data GitHub repository containing curated replication datasets.
