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Qiaomin Xie

Qiaomin Xie

Assistant Professor
gscholar website
lab website
linkedin website

Department

Industrial & Systems Engineering

Contact

M1002, Engineering Centers Building
1550 Engineering Dr
Madison, WI

  • PhD 2016, University of Illinois at Urbana-Champaign
  • BE 2010, Tsinghua University

  • Reinforcement Learning
  • Applied Probability
  • Stochastic Networks
  • Computer and Communication Networks

  • 2024 National Science Foundation, NSF CAREER Award
  • 2021 JPMorgan, JPMorgan Faculty Research Award
  • 2020 Google, Google Systems Research Award
  • 2017 Coordinated Science Lab (CSL), University of Illinois at Urbana-Champaign, Coordinated Science Lab (CSL) PhD Thesis Award
  • 2016 CMU, Rising Stars in EECS: an Academia Career Workshop for Women
  • 2016 IEEE INFOCOM, Student Travel Grant
  • 2015 University of Illinois at Urbana-Champaign, Yi-Min Wang and Pi-Yu Chung Research Award
  • 2011 IFIP WG7.3 Performance conference, Best Paper Award
  • 2009 Tsinghua University, Kaifeng Scholarship, First Prize for Overall Excellence
  • 2008 Tsinghua University, National Scholarship, First Prize for Academic Excellence
  • 2007 Tsinghua University, Kaifeng Scholarship, First Prize for Overall Excellence
  • 2006 Tsinghua University, Friend of Tsinghua Scholarship for Academic Excellence

  • Li, X., & Xie, Q. (2025). Coupling-based Convergence Diagnostic and Stepsize Scheme for Stochastic Gradient Descent. In Proceedings of the AAAI Conference on Artificial Intelligence.
  • Zhang, Y., Huo, D., Chen, Y., & Xie, Q. (2025). A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression.
  • Zhang, Y., & Xie, Q. (2024). Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation. In Reinforcement Learning Conference (RLC).
  • Wu, Y., McMahan, J., Zhu, X., & Xie, Q. (2024). Data Poisoning to Fake a Nash Equilibrium in Markov Games. In Proceedings of the AAAI Conference on Artificial Intelligence.
  • Huo, D. L., Chen, Y., & Xie, Q. (2024). Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 20447–20455).
  • Chen, Y., Zhang, X., Xie, Q., & Zhu, X. (2024). Exact Policy Recovery in Offline RL with Both Heavy-Tailed Rewards and Data Corruption. In Proceedings of the AAAI Conference on Artificial Intelligence.
  • McMahan, J., Wu, Y., Chen, Y., Zhu, X., & Xie, Q. (2024). Inception: Efficiently Computable Misinformation Attacks on Markov Games. In Reinforcement Learning Conference (RLC).
  • Pavse, B. S., Zurek, M., Chen, Y., Xie, Q., & Hanna, J. P. (2024). Learning to stabilize online reinforcement learning in unbounded state spaces. In International Conference on Machine Learning (ICML).
  • Wu, Y., McMahan, J., Chen, Y., Chen, Y., Zhu, X., & Xie, Q. (2024). Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value. In International Conference on Machine Learning (ICML).
  • McMahan, J., Wu, Y., Zhu, X., & Xie, Q. (2024). Optimal Attack and Defense for Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 14332–14340).

  • I SY E 320 - Simulation and Probabilistic Modeling (Spring 2025)
  • I SY E 321 - Simulation Modeling Laboratory (Spring 2025)
  • I SY E 624 - Stochastic Modeling Techniques (Spring 2025)
  • I SY E 890 - Pre-Dissertator's Research (Spring 2025)
  • I SY E 321 - Simulation Modeling Laboratory (Fall 2024)
  • I SY E 890 - Pre-Dissertator's Research (Fall 2024)
  • I SY E 890 - Pre-Dissertator's Research (Summer 2024)
  • I SY E 624 - Stochastic Modeling Techniques (Spring 2024)
  • I SY E 890 - Pre-Dissertator's Research (Spring 2024)
  • I SY E 320 - Simulation and Probabilistic Modeling (Fall 2023)
  • I SY E 321 - Simulation Modeling Laboratory (Fall 2023)
  • I SY E 890 - Pre-Dissertator's Research (Fall 2023)
  • I SY E 890 - Pre-Dissertator's Research (Summer 2023)