Dr. Weijie Su

Associate Professor

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Biography

Weijie Su is an Associate Professor at the Wharton School of the University of Pennsylvania, where he specializes in statistics and data science. He holds courtesy appointments in the Departments of Biostatistics, Epidemiology Informatics, Computer Information Science, and Mathematics. Su has actively contributed to the field of statistical learning, serving as the Founding Co-Editor for the journal Statistical Learning and as an Associate Editor for the Journal of Machine Learning Research. He is involved in advancing the study of machine learning through editorial roles and has made significant contributions in areas including operations research and applied statistics. Su's recent accolades include receiving the Outstanding Young Talent Award from the China Annual Review of Mathematics and being elected as a Fellow of the Institute of Mathematical Statistics. He will serve as the Program Chair for the Text Analysis Section of the American Statistical Association beginning January 1, 2026. Throughout his career, he has been involved in numerous high-profile invited presentations and lectures, as well as being recognized for his innovative research in ranking experiments and large language models.

Research Interests

Experience

Associate Professor

2015-08-01 — Present

University of Pennsylvania • Philadelphia, PA

Teaching and researching in statistics and data science, with courtesy appointments in multiple departments.

Requirements for University of Pennsylvania

Doctorate Program
Requirements
GPA Requirement
Required:3.6
GRE General
Verbal
Required:162
Quantitative
Required:162
Overall
Required:162
GMAT
Total Score
Required:728
Overall
Required:728
TOEFL
Total
Required:115
Prerequisites
Bachelor's degree or equivalent Strong quantitative background
Application Checklist
  • Academic Transcripts
  • Letters of Recommendation (2-3)
  • Resume/CV
  • Statement of Purpose
  • Essays
Specialization Notes

Wharton Doctoral programs cover fields like Finance, Marketing, Management, and Operations, Information and Decisions.