Yuhang Wu 吴雨航

I'm joining KEPL group at Cubist Systematic Strategies as a quantitative analyst in May 2025, and this academic homepage is no longer updated. I am currently a fourth-and-final year Ph.D. candidate in Operations Research at UC Berkeley's IEOR department and Berkeley AI Research Lab (BAIR). Previously, I received my B.S. degree in Statistics from School of Mathematical Sciences at Peking University. Contact University of California, Berkeley Email: wuyh ᴀᴛ berkeley.edu

Research Interests

I mainly work on applied statistics and probability, especially theory and applications of experimental design and causal inference.

Selected Publications and Manuscripts

1. A/B Test and Online Experiment Under Diminishing Marginal Effects: Regret Minimization and Statistical Inference Jingxu Xu, Yuhang Wu, Yingfei Wang, Chu Wang, Zeyu Zheng
   Submitted
2. Improving LLM Interpretability and Performance via Guided Embedding Augmentation for Sequential Recommendation Nanshan Jia, Chenfei Yuan, Yuhang Wu, Zeyu Zheng
   Submitted
3. Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Boostrapping Pu Yang, Yunzhen Feng, Ziyuan Chen, Yuhang Wu, Zhuoyuan Li
   Submitted
4. Debiasing Seller-Side Experiments via Multinomial Logit Models in Two-Sided Plaforms Chenyu Zhang, Yuhang Wu, Zeyu Zheng, Nian Si
   Extended abstract appeared in Conference on Digital Experimentation (CODE), 2024
   Submitted
5. Black-box Optimization with Simultaneous Statistical Inference for Optimal Performance Teng Lian, Jian-Qiang Hu, Yuhang Wu, Zeyu Zheng
   Submitted
6. Technical Note: Adaptive A/B Tests and Simultaneous Treatment Parameter Optimization Yuhang Wu, Zeyu Zheng, Guangyu Zhang, Zuohua Zhang, Chu Wang
   Extended abstract appeared in Marketplace Innovation Workshop (MIW), 2024
   Major revision at Operations Research
7. Large Language Model Enhanced Machine Learning Estimators for Classification Yuhang Wu, Yingfei Wang, Chu Wang, Zeyu Zheng
   Winter Simulation Conference (WSC), 2024
8. A Preliminary Study on Accelerating Simulation Optimization with GPU Implementation Jinghai He, Haoyu Liu, Yuhang Wu, Zeyu Zheng, Tingyu Zhu
   Winter Simulation Conference (WSC), 2024
9. Performance Evaluation and Stochastic Optimization with Gradually Changing Non-Stationary Data Yuhang Wu, Zeyu Zheng
   Operations Research, to appear
10. Non-stationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference Yuhang Wu, Zeyu Zheng, Guangyu Zhang, Zuohua Zhang, Chu Wang
    Management Science, 2024
11. Best Arm Identification with Fairness Constraints on Subpopulations Yuhang Wu, Zeyu Zheng, Tingyu Zhu
    Winter Simulation Conference (WSC), 2023
12. Non-stationary A/B Tests Yuhang Wu, Zeyu Zheng, Guangyu Zhang, Zuohua Zhang, Chu Wang
    SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022

Talks

1. Winter Simulation Conference, sessions on "Machine learning and AI in Analysis Methodology" and "Empirical Studies in Simuation Optimization", Orlando, US, December 2024

2. INFORMS Annual Meeting, session on "Exploration, Experimental Design, and Algorithmic Decision Making", Seattle, US, October 2024

3. Ninth Marketplace Innovation Workshop (MIW), Virtual, May 2024

4. Annual POMS Conference, session on "Experimentation", Minneapolis, US, April 2024

5. Winter Simulation Conference, San Antonio, US, December 2023

6. INFORMS Annual Meeting, sessions on "Experimental Design and A/B Tests in Marketplaces" and "Learning and optimization in nonstationary environments", Phoenix, US, October 2023

7. INFORMS Annual Meeting, session on "Data-driven Decision-making: Understanding and Improving Standard Policies", Indianapolis, US, October 2022

8. Amazon ATS Science Summit, Barcelona, Spain, September 2022

9. SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), session on "Mining, Inference and Learning", Washington, D.C., US, August 2022

Besides above conferences, my coauthors and I were also invited to present our work at MIT, Columbia University, Northwestern University, Cornell University, UChicago, USC, NUS, PKU, THU, Fudan University, SJTU, HKUST, and Amazon.

Experiences

1. Quantitative Analyst Internship, KEPL Group, Cubist Systematic Strategies
   New York, US, May-August 2024
   I had a really enjoyable summer there. If you are interested in KEPL group and quantitative finance, feel free to contact me.

Services

Reviewer for:

• Operations Research

• Naval Research Logistics

• Probability in the Engineering and Informational Sciences

• International Conference on Machine Learning (ICML)

• International Conference on Learning Representations (ICLR)

• Artificial Intelligence and Statistics (AISTATS)

• SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

• Winter Simulation Conference (WSC)

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