Publications

* : co-first author

1. A Review of Reinforcement Learning in Financial Applications
   Bai, Y.*, Gao, Y. *, Wan R. *, Zhang S. * and Song R. (2024) Annual Review of Statistics and Its Application
   
2. Robust Offline Policy Evaluation and Optimization with Heavy-Tailed Rewards
   Zhu, J. , Wan, R., Qi, Z., Luo, S. and Shi, C. (2024). AISTATS 2024.
   
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. (2024) AAAI 2024. (Oral Presentation)
   
4. Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring
   Wan, R.*, Liu, Y.*, McQueen, J., Hains D. and Song, R. (2023). KDD 2023
   
5. Multiplier Bootstrap-based Exploration
   Wan, R.*, Wei, H.*, Kveton, B. and Song, R. (2023). ICML 2023
   
6. Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework
   Wan, R*., Ge, L.* and Song, R. (2023). AISTATS 2023
   
7. Batch Policy Learning in Average Reward Markov Decision Processes
   Liao, P.*, Qi, Z.*, Wan, R., Klasnja, P. and Murphy S. (2022). Annals of Statistics (AoS)
   
8. A Multi-Agent Reinforcement Learning Framework for Treatment Effects Evaluation in Two-Sided Markets
   Shi, C., Wan, R., Song, G., Luo, S., Song, R. and Zhu, H. (2022). Annals of Applied Statistics (AOAS)
   
9. 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, B&E Section, American Statistical Association. Declined following the one-award-per-year policy)
   
10. Safe Exploration for Efficient Policy Evaluation and Comparison
    Wan, R., Kveton, B., and Song, R. (2022). ICML 2022
    
11. Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
    Wan, R., Ge, L. and Song, R. (2021). NeurIPS 2021
    
12. Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
    Wan, R., Zhang, X. and Song, R. (2021). KDD 2021
    (Norman Breslow Young Investigator Award, American Statistical Association )
    
13. Deeply-Debiased Off-Policy Interval Estimation
    Shi, C.*, Wan, R*., Chernozhukov, V. and Song, R. (2021). ICML 2021. (Long oral presentation, rate 3%)
    
14. Pattern Transfer Learning for Reinforcement Learning in Order Dispatching
    Wan, R*., Zhang, S.*, Shi, C., Luo, S. and Song, R. (2021). IJCAI 2021, RLITS workshop. (Best paper award)
    
15. 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. Zero-Inflated Bandits
   Wei, H.*, Wan, R.*, Shi, L. and Song, R.
   
2. STEEL: Singularity-aware Reinforcement Learning
   Chen, X., Qi, Z. and Wan, R..
   
3. 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. Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring.
   Wan, R.*, Liu, Y.*, McQueen, J., Hains D. and Song, R. (2023) CSS 2023 (Best Paper Award) and AMLC 2023.
   
3. Data-driven substitution aware promise tuning model and promise extension experiment
   Giannakakis, I., Svoboda , R., Wan, R.*, Gu, J., Yao, J. (2023) AMLC 2023
   
4. 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
   
5. 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
   
6. Deep Inventory Control Policy for Perishables
   Wan, R., Giannakakis, I., Sisikoglu E., Jiang T., Goyal, V., Song, R. and Gu, J. (2022). CSS 2022 (Long talk) and AMLC 2022.
   
7. Reinforcement Learning for Replaceability Index Estimation and Assortment Optimization.
   Wan, R., Giannakakis, I., Gu, J. and Song, R. (2021). CSS 2021 (Spotlight talk, rate 4.8%) and AMLC 2021.
   

Other Contributions

• Our tutorial book on Causal Decision Making is online! Check Causal Decision Making

• Norman Breslow Young Investigator Award, American Statistical Association
• Gertrude M. Cox Academic Achievement Award (1st place in the Ph.D. Qualifier Exam), NC State University
• Finalist in Reinforcement Learning Competition Track in KDD Cup 2020. (Ranked 7th out of 1000+ teams)
• Buqing Su Honor Graduate, Fudan University
• First Prize, Undergraduate Scholarship, Fudan University

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