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Dr. GAO Siyang (高思陽博士)

BS(PKU), PhD(Univ of Wisconsin)

Professor

Associate Head of Department of Systems Engineering

Contact Information

Office:  AC1-P6611
Phone: 34424759
Email: siyangao@cityu.edu.hk
Web: Google Scholar

Research Interests

  • Embodied AI
  • Large language models
  • Simulation modeling and optimization
  • Digital twin
  • Machine learning
  • Healthcare management
Dr. Siyang Gao is a Professor in the Departments of Systems Engineering and Data Science at City University of Hong Kong, where he also serves as Associate Head of the Department of Systems Engineering. He received his B.S. from Peking University and his Ph.D. from the University of Wisconsin–Madison. His research focuses on embodied AI, large language models, simulation optimization, digital twins, machine learning, and healthcare management. Dr. Gao has published extensively in leading journals and conferences, including OR, MSOM, POM, IJOC, Automatica, IEEE Transactions, ICML, NeurIPS, ICLR, KDD, EMNLP, etc. His work has received multiple international best paper awards and competitive research grants. He is also deeply engaged in academic service, serving as an Associate Editor for international journals and taking leadership roles in major international conferences.


Publications Show All Publications Show Prominent Publications


Journal

  • Li, J. , Du, J. , Gao, S. , Ye, Q. & Jiang, G. (in press). Enhancing electric vehicle charging station design using multi-fidelity simulations. Production and Operations Management.
  • Yang, L. , Gao, S. , Li, C. & Wang, Y. (2025). Stochastically constrained best arm identification with Thompson sampling. Automatica. 176. 112223 .
  • Du, J. , Gao, S. & Chen, C.-H. (2024). A contextual ranking and selection method for personalized medicine. Manufacturing & Service Operations Management. 26(1). 167 - 181.
  • Li, Y. , Gao, S. & Shi, Z. (2023). Asymptotic optimality of myopic ranking and selection procedures. Automatica. 151. 110896 .
  • Li, C. , Gao, S. & Du, J. (2023). Convergence Analysis of Stochastic Kriging-Assisted Simulation with Random Covariates. INFORMS Journal on Computing. 35(2). 386 - 402.
  • Li, Y. & Gao, S. (2023). Convergence Rate Analysis for Optimal Computing Budget Allocation Algorithms. Automatica. 153. 111042 .
  • Chen, W. , Gao, S. , Chen, W. & Du, J. (2023). Optimizing Resource Allocation in Service Systems via Simulation: A Bayesian Formulation. Production and Operations Management. 32(1). 65 - 81.
  • Gao, F. , Shi, Z. , Gao, S. & Xiao, H. (2019). Efficient simulation budget allocation for subset selection using regression metamodels. Automatica. 106. 192 - 200.
  • Gao, S. , Shi, L. & Zhang, Z. (2018). A peak-over-threshold search method for global optimization. Automatica. 89. 83 - 91.
  • Xiao, H. & Gao, S. (2018). Simulation budget allocation for selecting the top-m designs with input uncertainty. IEEE Transactions on Automatic Control. 63(9). 3127 - 3134.
  • Gao, S. , Chen, W. & Shi, L. (2017). A new budget allocation framework for the expected opportunity cost. Operations Research. 65. 787 - 803.
  • Gao, S. & Chen, W. (2017). A partition-based random search for stochastic constrained optimization via simulation. IEEE Transactions on Automatic Control. 62. 740 - 752.
  • Gao, S. & Chen, W. (2017). Efficient feasibility determination with multiple performance measure constraints. IEEE Transactions on Automatic Control. 62. 113 - 122.
  • Gao, S. , Xiao, H. , Zhou, E. & Chen, W. (2017). Robust ranking and selection with optimal computing budget allocation. Automatica. 81. 30 - 36.
  • Xiao, H. & Gao, S. (2017). Simulation budget allocation for simultaneously selecting the best and worst subsets. Automatica. 84. 117 - 127.
  • Gao, S. & Chen, W. (2016). A new budget allocation framework for selecting top simulated designs. IIE Transactions. 48. 855 - 863.
  • Gao, S. & Chen, W. (2015). Efficient subset selection for the expected opportunity cost. Automatica. 59. 19 - 26.
  • Gao, S. & Shi, L. (2015). Selecting the best simulated design with the expected opportunity cost bound. IEEE Transactions on Automatic Control. 60(10). 2785 - 2790.

Conference Paper

  • Wang, Y. , Fan, S. , Wang, M. , Gao, S. , Wang, C. & Yin, N. (2026). DAMR: Efficient and adaptive context-aware knowledge graph question answering with LLM-guided MCTS. 2026 International Conference on Learning Representations (ICLR).
  • Qiao, Z. , Yang, R. , Lyu, J. , Li, X. , Dai, Z. , Yang, Z. , Gao, S. & Qiu, S. (2026). Dual-robust cross-domain offline reinforcement learning against dynamics shifts. 2026 International Conference on Learning Representations (ICLR).
  • Qiao, Z. , Lyu, J. , Lyu, B. , Shu, Y. , Shu, Y. , Gao, S. & Qiu, S. (2026). Model-based offline RL via robust value-aware model learning with implicitly differentiable adaptive weighting. 2026 International Conference on Learning Representations (ICLR).
  • Qiao, Z. , Lyu, J. , Bai, C. , Wang, P. , Gao, S. & Qiu, S. (2026). Unifying value alignment and assignment in cross-domain offline reinforcement learning with heterogeneous datasets. 2026 International Conference on Machine Learning (ICML).
  • Wang, Y. , Zhang, K. , Wang, M. , Gao, S. & Yin, N. (2026). USBD: Universal structural basis distillation for source-free graph domain adaptation. 2026 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).
  • Chen, S. , Zhu, T. , Wang, Z. , Zhang, J. , Wang, K. , Zhou, R. , Gao, S. , Xiao, T. , Teh, Y. W. , He, J. & Li, M. (2026). Why do LLM agents fail in exploring new environments? A world-modeling perspective. 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP).
  • Chen, S. , Zhang, J. , Zhu, T. , Liu, W. , Gao, S. , Xiong, M. , Li, M. & He, J. (2025). Bring reason to vision: Understanding perception and reasoning through model merging. International Conference on Machine Learning (ICML).
  • Chen, S. , Zhu, T. , Zhou, R. , Zhang, J. , Gao, S. , Niebles, J. C. , Geva, M. , He, J. , Wu, J. & Li, M. (2025). Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas. International Conference on Machine Learning (ICML).
  • Chen, S. , Xiong, M. , Liu, J. , Wu, Z. , Xiao, T. , Gao, S. & He, J. (2024). In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation. International Conference on Machine Learning (ICML).
  • Yu, Z. , Dai, L. , Xu, S. , Gao, S. & Ho, C. (2023). Fast Bellman updates for Wasserstein distributionally robust MDPs. Advances in Neural Information Processing Systems (NeurIPS). 36. (pp. 30554 - 30578).
  • Chen, S. , Zhao, Y. , Zhang, J. , Chern, I.-C. , Gao, S. , Liu, P. & He, J. (2023). FELM: Benchmarking factuality evaluation of large language lodels. Advances in Neural Information Processing Systems (NeurIPS). 36. (pp. 44502 - 44523).
  • Yang, L. , Gao, S. & Ho, C. (2023). Improving the knowledge gradient algorithm. Advances in Neural Information Processing Systems (NeurIPS). 36. (pp. 61747 - 61758).
  • Li, Y. & Gao, S. (2022). On the finite-time performance of the knowledge gradient algorithm. International Conference on Machine Learning (ICML). (pp. 12741 - 12764).


External Services


Professional Activity

  • 2026 - Now, Associate Editor, IISE Transactions.
  • 2021 - 2026, Associate editor, IEEE Transactions on Automation Science and Engineering.
  • 2021 - Now, Associate editor, Journal of Simulation.


For prospective students

  • I am looking for qualified Ph.D. students to do research in embodied AI, large language models, simulation optimization, machine learning, and reinforcement learning. If you are interested, please send your CV and transcript to my email (siyangao@cityu.edu.hk) for consideration.


Links



Last update date : 22 Aug 2026