Dr. Siyang Gao received a B.S. in Statistics and Probability from School of Mathematics at Peking University in 2009 and a Ph.D. in Industrial Engineering at University of Wisconsin-Madison in 2014. His research interests include simulation modeling and optimization, large language models, digital twin, machine learning, and healthcare management.
Journal
- 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
- 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
- 2021 - Now, 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 simulation optimization, large language models, machine learning, and reinforcement learning. If you are interested, please send your CV and transcript to my email (siyangao@cityu.edu.hk) for consideration.
Last update date :
30 Aug 2025
