Zishun's Homepage

Zishun Yu

Computer Science,
University of Illinois at Chicago
Chicago, IL 60607
E-mail: zyu32 [@] uic [DOT] edu
[LinkedIn] [Kaggle] [Google Scholar]

About Me

I am a computer science Ph.D. student at the University of Illinois at Chicago (UIC), advised by Prof. Xinhua Zhang, since 2021. Also, I am fortunate to collaborate with Prof. Ian Kash and Prof. Lev Reyzin. My research interest spans reinforcement learning and optimization.

Prior to that, I received my master's degree in Industrial Engineering & Operations Research from UIC, under the advisory of Prof. Mengqi Hu, and my bachelor's degree from Huazhong University of Science and Technology.


* equal contribution/main student author
in alphabetical order

  • [1] \(\mathcal{B}\)-Coder: On Value-Based Deep Reinforcement Learning for Program Synthesis. [PDF]
    Zishun Yu, Yunzhe Tao, Liyu Chen, Tao Sun and Hongxia Yang
    ICLR [Spotlight] (International Conference on Learning Representations), 2024
    FMDM@NeurIPS (Foundation Models for Decision Making Workshop), 2023

  • [2] Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs. [PDF]
    Zishun Yu*, with Ian A. Kash and Lev Reyzin
    ALT (International Conference on Algorithmic Learning Theory), 2024

  • [3] Actor-Critic Alignment for Offline-to-Online Reinforcement Learning. [PDF][Code]
    Zishun Yu and Xinhua Zhang
    ICML (International Conference on Machine Learning), 2023

  • [4] Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats. [PDF][Code]
    Hongwei Jin, Zishun Yu and Xinhua Zhang
    NeurIPS (Neural Information Processing Systems), 2022

  • [5] Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound. [PDF][Code]
    Hongwei Jin, Zishun Yu and Xinhua Zhang
    UAI (Uncertainty in Artificial Intelligence), 2022

  • [6] Deep Reinforcement Learning with Graph Representation for Vehicle Repositioning. [PDF]
    Zishun Yu and Mengqi Hu
    IEEE-ITS (IEEE Transactions on Intelligent Transportation Systems), 2021

Professional Experience

  • Research Scientist Intern
    TikTok/ByteDance, Chicago IL
    Aug 2023 - Sept 2023

  • Research Scientist Intern
    TikTok/ByteDance, Bellevue WA
    May 2023 - Aug 2023


  • MITACS Globalink Undergraduate Research Internship (Summer 2016)
    York University, Canada

Professional Services

Conference Reviewer:

  • Conference on Neural Information Processing Systems (NeurIPS) , 2022, 2023

  • International Conference on Machine Learning (ICML) , 2023

  • International Conference on Learning Representations (ICLR) , 2023, 2024

  • International Conference on Artificial Intelligence and Statistics (AISTATS) , 2023, 2024

  • Conference on Uncertainty in Artificial Intelligence (UAI) , 2023

Journal Reviewer:

  • Transactions on Machine Learning Research (TMLR) , 2023 -

  • IEEE Computational Intelligence Magazine (CIM) , 2023 -


Kaggle competition Master¹.

  • [Kaggle] Santa's Annual Optimization Challenge 2022 [Link]
    Gold medal          (rank 8/875)
    Robotics, Planning, Optimization, Combinatorics, TSP

  • [Kaggle] Santa's Annual Optimization Challenge 2021 [Link]
    Silver medal        (top 4%)
    Optimization, Combinatorics, Superpermutation, Coloured TSP

  • [Kaggle] Santa's Annual Optimization Challenge 2019 [Link]
    Silver medal        (top 5%)
    Optimization, Combinatorics, Assignment Problem, Mixed Integer Programing

  • [Kaggle] Catheter and Line Position Challenge [Link]
    Bronze medal     (top 8%)
    Classification, Computer Vision, Medical Image

(¹Kaggle official ranking system.)