About Me

I am an incoming Ph.D. student in Computer Science at Cornell University, where I will be co-advised by Thorsten Joachims and Nathan Kallus. I am also working with Yusuke Narita (Yale University) and some tech companies in Tokyo for large-scale empirical studies. I recently completed my bachelor’s degree in Industrial Engineering and Economics at the Tokyo Institute of Technology.

My research lies at the intersection of machine learning and causal inference called counterfactual learning. I am interested in the counterfactual nature of logged bandit feedback and human behavior data obtained from interactive systems, and ways of using biased real-world datasets to assist safe and better decision making in the wild.

Research Interests

  • Counterfactual Learning and Evaluation
    • Off-Policy Evaluation (Bandit / Reinforcement Learning)
    • Heterogeneous Treatment Effect Prediction
    • Offline Recommender Learning and Evaluation
  • Learning from Human Behavior Data
  • Statistical Machine Learning


  • Cornell University (2021-2026)
  • Tokyo Institute of Technology (2016-2021)
    • B.Eng. in Industrial Engineering and Economics
    • Research Topics: Counterfactual Learning, Causal Inference, Recommender Systems


  • 2021.06: Our tutorial proposal: “Counterfactual Evaluation and Learning for Recommender Systems: Foundations, Implementations, and Recent Advances” has been accepted at RecSys2021.
  • 2021.05: Our paper: “Optimal Off-Policy Evaluation from Multiple Logging Policies” has been accepted at ICML2021.
  • 2021.03: I made an invited talk about the Open Bandit Project at the Tiger Talk (RMIT University)
  • 2021.02: We released awesome-offline-rl, which is a collection of research and review papers for offline reinforcement learning (offline rl).
  • 2020.08: We released Open Bandit Dataset (a large-scale dataset for bandit algorithms) and Open Bandit Pipeline (python package for bandit algorithms and off-policy evaluation).
  • 2020.07: My solo paper: “Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions” has been accepted at RecSys2020.
  • 2020.06: Our paper: “Unbiased Lift-based Bidding System” has been accepted at AdKDD2020.
  • 2020.06: Our paper: “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models” has been accepted at ICML2020.
  • 2020.04: My solo paper: “Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback” has been accepted at SIGIR2020.
  • 2019.10: Our paper “Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback” has been accepted at WSDM2020.