[Preprints][Journal Papers][Conference Papers]

Please see Google Scholar for an up-to-date list of publications.

Preprints

  1. Haoxiang Ma, Shuo Han, Ahmed Hemida, Charles A. Kamhoua, and Jie Fu, “Adaptive Incentive Design for Markov Decision Processes with Unknown Rewards,” Aug. 2024.
  2. Shuo Wu, Haoxiang Ma, Jie Fu, and Shuo Han, “Robust Reward Design for Markov Decision Processes,” Jun. 2024. [pdf]

[Back to Top]

Journal Papers

  1. Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Shaofeng Zou, and Fei Miao, “What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?,” Transactions on Machine Learning Research, Feb. 2024. [pdf]
  2. Chongyang Shi, Shuo Han, Michael Dorothy, and Jie Fu, “Active Perception With Initial-State Uncertainty: A Policy Gradient Method,” IEEE Control Systems Letters, vol. 8, pp. 3147–3152, 2024.
  3. Sihong He, Songyang Han, Sanbao Su, Shuo Han, Shaofeng Zou, and Fei Miao, “Robust Multi-Agent Reinforcement Learning with State Uncertainty,” Transactions on Machine Learning Research, Jun. 2023. [pdf]
  4. Sihong He, Zhili Zhang, Shuo Han, Lynn Pepin, Guang Wang, Desheng Zhang, John A. Stankovic, and Fei Miao, “Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 5, pp. 5199–5215, May 2023. [pdf]
  5. Sumukha Udupa, Abhishek N. Kulkarni, Shuo Han, Nandi O. Leslie, Charles A. Kamhoua, and Jie Fu, “Synthesizing Attack-Aware Control and Active Sensing Strategies Under Reactive Sensor Attacks,” IEEE Control Systems Letters, vol. 7, pp. 265–270, 2023. [pdf]
  6. Haoxiang Ma, Shuo Han, Charles A. Kamhoua, and Jie Fu, “Optimizing Sensor Allocation Against Attackers With Uncertain Intentions: A Worst-Case Regret Minimization Approach,” IEEE Control Systems Letters, vol. 7, pp. 2863–2868, 2023. [pdf]
  7. Shuo Han, “Gradient Methods With Dynamic Inexact Oracles,” IEEE Control Systems Letters, vol. 5, no. 4, pp. 1163–1168, Oct. 2021. [pdf]
  8. Fei Miao, Sihong He, Lynn Pepin, Shuo Han, Abdeltawab Hendawi, Mohamed E Khalefa, John A. Stankovic, and George Pappas, “Data-Driven Distributionally Robust Optimization For Vehicle Balancing of Mobility-on-Demand Systems,” ACM Transactions on Cyber-Physical Systems, vol. 5, no. 2, Jan. 2021. [pdf]
  9. Shuo Han, “Systematic Design of Decentralized Algorithms for Consensus Optimization,” IEEE Control Systems Letters, vol. 3, no. 4, pp. 966–971, Oct. 2019. [pdf]
  10. Fei Miao, Shuo Han, Shan Lin, Qian Wang, John Stankovic, Abdeltawab Hendawi, Desheng Zhang, Tian He, and George J. Pappas, “Data-Driven Robust Taxi Dispatch under Demand Uncertainties,” IEEE Transactions on Control Systems Technology, vol. 27, no. 1, pp. 175–191, Jan. 2019. [pdf]
  11. Shuo Han and George J. Pappas, “Privacy in Control and Dynamical Systems,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 1, no. 1, 2018. [pdf]
  12. Shuo Han, Ufuk Topcu, and George J. Pappas, “Differentially private distributed constrained optimization,” IEEE Transactions on Automatic Control, vol. 62, no. 1, pp. 50–64, 2017. [pdf]
  13. Fragkiskos Koufogiannis, Shuo Han, and George J. Pappas, “Gradual release of sensitive data under differential privacy,” Journal of Privacy and Confidentiality, vol. 7, no. 2, pp. 23–52, 2017. [pdf]
  14. Fei Miao, Shuo Han, Shan Lin, John A. Stankovic, Desheng Zhang, Sirajum Munir, Hua Huang, Tian He, and George J. Pappas, “Taxi Dispatch with Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach,” IEEE Transactions on Automation Science and Engineering, vol. 13, no. 2, pp. 463–478, 2016. [pdf]
  15. Shuo Han, Victor M. Preciado, Cameron Nowzari, and George J. Pappas, “Data-driven network resource allocation for controlling spreading processes,” IEEE Transactions on Network Science and Engineering, vol. 2, no. 4, pp. 127–138, 2015. [pdf]
  16. Shuo Han, Molei Tao, Ufuk Topcu, Houman Owhadi, and Richard M. Murray, “Convex optimal uncertainty quantification,” SIAM Journal on Optimization, vol. 25, no. 3, pp. 1368–1387, 2015. [pdf]

[Back to Top]

Conference Papers

  1. Yansong Li and Shuo Han, “Efficient Collaboration with Unknown Agents: Ignoring Similar Agents without Checking Similarity,” in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024. (extended abstract) [pdf]
  2. Haoxiang Ma, Chongyang Shi, Shuo Han, Michael Dorothy, and Jie Fu, “Covert Planning aganist Imperfect Observers,” in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024. [pdf]
  3. Sihong He, Yue Wang, Shuo Han, Shaofeng Zou, and Fei Miao, “A Robust and Constrained Multi-Agent Reinforcement Learning Framework for Electric Vehicle AMoD Systems,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023. [pdf]
  4. Haoxiang Ma, Shuo Han, Chales Kamhoua, and Jie Fu, “Optimal Resource Allocation for Proactive Defense with Deception in Probabilistic Attack Graphs,” in Conference on Decision and Game Theory for Security (GameSec), 2023. [pdf]
  5. Sihong He, Shuo Han, and Fei Miao, “Robust Electric Vehicle Balancing of Autonomous Mobility-on-demand System: A Multi-Agent Reinforcement Learning Approach,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023. [pdf]
  6. Lening Li, Haoxiang Ma, Shuo Han, and Jie Fu, “Synthesis of Proactive Sensor Placement In Probabilistic Attack Graphs,” in American Control Conference, 2023. [pdf]
  7. Yansong Li and Shuo Han, “Solving Strongly Convex and Smooth Stackelberg Games Without Modeling the Follower,” in American Control Conference, 2023. [pdf]
  8. Chongyang Shi, Shuo Han, and Jie Fu, “Quantitative Planning with Action Deception in Concurrent Stochastic Games,” in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2023. [pdf]
  9. Yansong Li and Shuo Han, “Accelerating Model-Free Policy Optimization Using Model-Based Gradient: A Composite Optimization Perspective,” in Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022, pp. 304–315. [pdf]
  10. Abhishek Kulkarni, Shuo Han, Nandi Leslie, Charles Kamhoua, and Jie Fu, “Qualitative Planning in Imperfect Information Games with Active Sensing and Reactive Sensor Attacks: Cost of Unawareness,” in IEEE Conference on Decision and Control, 2021. [pdf]
  11. Jieren Deng, Chenghong Wang, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, and Caiwen Ding, “A Secure and Efficient Federated Learning Framework for NLP,” in Conference on Empirical Methods in Natural Language Processing, 2021. [pdf]
  12. Shuo Han, “Computational Convergence Analysis of Distributed Gradient Tracking for Smooth Convex Optimization Using Dissipativity Theory,” in American Control Conference, 2019. [pdf]
  13. Shuo Han, Ufuk Topcu, and George J. Pappas, “Quantification on the efficiency gain of automated ridesharing services,” in American Control Conference, 2017.
  14. Fei Miao, Shuo Han, Abdeltawab Hendawi, Mohamed E. Khalefa, John A. Stankovic, and George J. Pappas, “Data-driven distributionally robust vehicle balancing with dynamic region partition,” in ACM/IEEE International Conference on Cyber-Physical Systems, 2017.
  15. Jorge Cortés, Geir E. Dullerud, Shuo Han, Jerome Le Ny, Sayan Mitra, and George J. Pappas, “Differential privacy in control and network systems,” in IEEE Conference on Decision and Control, 2016. (tutorial paper)
  16. Shuo Han, Ufuk Topcu, and George J. Pappas, “Event-based information-theoretic privacy: A case study of smart meters,” in American Control Conference, 2016.
  17. Jie Fu, Shuo Han, and Ufuk Topcu, “Optimal control in Markov decision processes via distributed optimization,” in IEEE Conference on Decision and Control, 2015. [pdf]
  18. Shuo Han, Ufuk Topcu, and George J. Pappas, “A sublinear algorithm for barrier-certificate-based data-driven model validation of dynamical systems,” in IEEE Conference on Decision and Control, 2015. [pdf]
  19. Shuo Han, Ufuk Topcu, and George J. Pappas, “An approximately truthful mechanism for electric vehicle charging via joint differential privacy,” in American Control Conference, 2015. [pdf]
  20. Fei Miao, Shuo Han, Shan Lin, and George J. Pappas, “Taxi dispatch under model uncertainties,” in IEEE Conference on Decision and Control, 2015. [pdf]
  21. Shuo Han, Ufuk Topcu, and George J. Pappas, “Differentially private distributed protocol for electric vehicle charging,” in Annual Allerton Conference on Communication, Control, and Computing, 2014. [pdf]
  22. Shuo Han, Ufuk Topcu, and George J. Pappas, “Differentially private convex optimization with piecewise affine objectives,” in IEEE Conference on Decision and Control, 2014. [pdf]
  23. Fragkiskos Koufogiannis, Shuo Han, and George J. Pappas, “Computation of privacy-preserving prices in smart grids,” in IEEE Conference on Decision and Control, 2014. [pdf]
  24. Shuo Han, Ufuk Topcu, Molei Tao, Houman Owhadi, and Richard M. Murray, “Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids,” in American Control Conference, 2013. Best Student Paper Finalist [pdf]
  25. Shuo Han and Richard M. Murray, “Containment indicator function construction via numerical conformal mapping,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011.
  26. Shuo Han, Andrea Censi, Andrew D. Straw, and Richard M. Murray, “A bio-plausible design for visual pose stabilization,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010.
  27. Andrea Censi, Shuo Han, Sawyer B. Fuller, and Richard M. Murray, “A bio-plausible design for visual attitude stabilization,” in IEEE Conference on Decision and Control, 2009.
  28. Shuo Han, Andrew D. Straw, Michael H. Dickinson, and Richard M. Murray, “A real-time helicopter testbed for insect-inspired visual flight control,” in IEEE International Conference on Robotics and Automation, 2009.

[Back to Top]