Runzhe Wan

Runzhe Wan

Applied Scientist

Amazon Inc.

Biography

I am currently an Senior Applied Scientist at Amazon. I obtained my Ph.D. degree in Statistics at North Carolina State University, advised by Dr. Rui Song. Previously, I received my B.S. in Mathematics from Fudan University, China in May 2017.

My current research interests center around optimal decision-making under uncertainty. Such a decision may have long-term impacts, need to be personalized, and can be evaluated/learned either during online interactions or from offline data.

At Amazon, I am collaborating with multiple product teams on inventing and productionizing advanced methodology to revolutionize critical systems that have direct impacts on Amazon business.

Education
  • Ph.D. in Statistics

    North Carolina State University, 2022

  • B.S. in Mathematics

    Fudan University, 2017

Experience
  • Amazon Inc.

    Senior Applied Scientist

    April.2024 -- Present

  • Amazon Inc.

    Applied Scientist

    April.2022 -- March.2024

  • Amazon Inc.

    Applied/Research Scientist Intern

    May.2020 -- Feb.2022 (part-time within semesters)

  • Bell Labs

    Research Intern

    Jun.2019 - Aug.2019

Research

Selected Publications

* : Equal Contribution

  1. Zero-Inflated Bandits
    Wei, H.*, Wan, R.*, Shi, L. and Song, R. (2025). ICML 2025
  2. Know When to Fold: Futility-Aware Early Termination in Online Experiments
    Liu, Y.*, Wan, R.*, Huang, Y., McQueen, J., Hains, D., Gu, J., and Song, R. (2025). WWW 2025
  3. Robust Offline Policy Evaluation and Optimization with Heavy-Tailed Rewards
    Zhu, J. , Wan, R., Qi, Z., Luo, S. and Shi, C. (2024). AISTATS 2024.
  4. Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches
  5. Multiplier Bootstrap-based Exploration
    Wan, R.*, Wei, H.*, Kveton, B. and Song, R. (2023). ICML 2023
  6. Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies
    Wan, R., Li Y., Lu, W. and Song, R. (2022). Journal of Econometrics (JOE)
    (Won the Best Student Paper Award, Business&Econ Section, American Statistical Association. Declined following the one-award-per-year policy)
  7. Safe Exploration for Efficient Policy Evaluation and Comparison
    Wan, R., Kveton, B., and Song, R. (2022). ICML 2022
  8. Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
    Wan, R., Ge, L. and Song, R. (2021). NeurIPS 2021
  9. Deeply-Debiased Off-Policy Interval Estimation
    Shi, C.*, Wan, R*., Chernozhukov, V. and Song, R. (2021). ICML 2021. (Long oral, rate 3%)
  10. Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
    Shi, C., Wan, R., Song, R., Lu, W. and Leng, L. (2020). ICML 2020

Under Review / Revision

  1. A Review of Causal Decision Making
    Ge, L.*, Cai, H.*, Wan, R.*, Xu, Y.*, and Song, R.
  2. Heterogeneous Synthetic Learner for Panel Data
    Shen, Y., Wan, R., Cai, H. and Song, R.

Internal Publications (Amazon)

  1. Know When to Fold: Futility-aware Early Termination in Online Experiments.
    Wan, R.*, Liu, Y.*, Huang Y., McQueen, J., Hains D., Gu J. and Song, R. (2023) CSS 2024 (Best Paper Award)
  2. Data-driven substitution aware promise tuning model and promise extension experiment
    Giannakakis, I., Svoboda , R., Wan, R.*, Gu, J., Yao, J. (2023) AMLC 2023
  3. Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches
    Liu, Y.*, Wan, R.*, McQueen, J., Hains D., Gu, J., and Song, R. (2023) Econ Summit 2023
  4. Continuous Monitoring of A/B Tests: A Meta Analysis
    Liu, Y.*, Wan, R.*, McQueen, J., Song, R, Hains D. and Richardson, T. (2023) AMLC 2023

Other Contributions

Awards / Honors

Professional Services

Reviewer:

  • Conferences: NeurIPS, ICML, ICLR, UAI, AISTATS, KDD, IJCAI, AAAI, SDM
  • Journals: Annals of Statistics, Journal of the American Statistical Association (T&M), Journal of the American Statistical Association (book review), Journal of the Royal Statistical Society (Series B), EJS, STPA, Transactions on Machine Learning Research (TMLR)

PC member: RLITS Workshop at IJCAI 2021, Online Marketplaces Workshop at KDD 2022/2023