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Ying Jin is an Assistant Professor in the Department of Statistics and Data Science at the University of Pennsylvania. His research focuses on uncertainty quantification, distribution-free inference, causal inference, selective inference, and generalizability. With a strong background in statistical methodology, he has contributed to a variety of advanced statistical techniques that address modern challenges in data analysis and inference. His work has been recognized with prestigious awards including the IMS Lawrence D. Brown PhD Student Award and the Jack Youden Prize from the American Society for Quality. Jin's publications appear in leading journals, showcasing his innovative approaches and applications in statistical research. He is committed to educating the next generation of data scientists and statisticians, teaching courses that cover modern data mining techniques and their applications across various fields.
Wharton Doctoral programs cover fields like Finance, Marketing, Management, and Operations, Information and Decisions.