Sikun Xu

I am a Ph.D. candidate at Olin Business School, Washington University in St. Louis. I’m fortunate to be advised by Prof. Dennis Zhang and Prof. Raphael Thomadsen. I study how firms can make reliable decisions using modern machine learning and AI systems when the available data is noisy and high-dimensional.

Contacts

• sikun [at] wustl [dot] edu
• xusikun96 [at] gmail [dot] com

Education

• Olin Business School, Washington University in St. Louis (2021-now)
  • Ph.D. Candidate
  • Dissertation Title: Data-Driven Business Decision-Making with Causal Inference and Machine Learning
• Columbia University in the City of New York (2019-2020)
  • M.S. in Operations Research
  • Data Science Institute Scholar
• Shanghai Jiao Tong University (2015-2019)
  • B.S. in Industrial Engineering

Working Papers

• A Causal Approach to Representation Learning for Unstructured Data, with Zhenling Jiang, Zhengling Qi, and Dennis Zhang, Major Revision in Management Science
  • Accepted to 19th Annual Bass FORMS Conference (2025)
• The Winner’s Curse in Data-Driven Decision-Making: Evidence and Solutions, with Raphael Thomadsen and Dennis Zhang
  • Accepted to SICS 2025

Conference Proceedings

1. Sikun Xu, Ruoyi Ma, Daniel K. Molzahn, Hassan Hijazi, and Cédric Josz. “Verifying Global Optimality of Candidate Solutions to Polynomial Optimization Problems using a Determinant Relaxation Hierarchy.” 60th IEEE Conference on Decision and Control (2021).

Work in Progress

• SOTA or Luck? The Winner’s Curse in LLM Leaderboards, with Raphael Thomadsen, Dennis Zhang, and Heng Zhang

Conference Presentations

• The Winner’s Curse in Data-Driven Decision-Making: Evidence and Solutions
  • 2025 INFORMS Annual Meeting (Atlanta)
  • 2025 INFORMS Marketing Science Conference (Washington, D.C.)
  • 2024 Conference on Artificial Intelligence, Machine Learning, and Business Analytics (Yale)
• A Causal Approach to Representation Learning for Unstructured Data
  • 2025 ISMS Marketing Science Conference (Washington, D.C.)
  • 2024 INFORMS Annual Meeting (Seattle, Sesssion Chair)
  • 2024 POMS Annual Conference (Minneapolis)
  • 2023 INFORMS Annual Meeting (Phoenix)
• Data-driven security selection for wealth management
  • 2023 POMS Annual Conference (Orlando)
  • 2022 INFORMS Annual Meeting (Indianapolis)
• Verifying global optimality of candidate solutions to polynomial optimization problems using a determinant relaxation hierarchy [slide]
  • 2021 INFORMS Annual Meeting (Virtual)

Teaching

Guest Lecturer

• Washington University in St. Louis (MGT680E, 2024 Fall)
• Columbia University (IEOR4721, 2022 Spring)
• Columbia University (IEOR4721, 2021 Summer)

Teaching Assistants

Columbia University in the City of New York

• IEOR4742 Deep Learning; FL2020
• IEOR4525 Machine Learning; SP2020

Washington University in St. Louis

• SCOT519E Revenue Management; FL2022
• SCOT5704 Operations Management; FL2022
• SCOT500D Project Management; FL2022, SP2023
• SCOT500M Supply Chain Analytics: Stochastic Models; SP2023, SP2024
• SCOT400D Supply Chain Analytics; SP2023
• SCOT558 Advanced Operations Strategy; FL2023
• SCOT356 Operations and Manufacturing Management; FL2024
• MGT680E AI & Machine Learning Business Applications; FL2024

Academic Services

• Session Chair at INFORMS Annual Meeting (2024)
• Reviewer for Journal of Investment Strategies