Dr. GAO Siyang (高思陽博士)

BS(PKU), PhD(Univ of Wisconsin)

Associate Professor

Contact Information

Office:	AC1-P6611
Phone:	34424759
Email:	[email protected]
Web:	Google Scholar

Research Interests

Simulation modeling and optimization
Large language models
Machine learning
Healthcare management

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, applied probability, machine learning, and healthcare management.

Publications Show All Publications Show Prominent Publications

Journal

Du, J. , Gao, S. & Chen, C.-H. (in press). A contextual ranking and selection method for personalized medicine. Manufacturing & Service Operations Management.
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. , Xiong, M. , Liu, J. , Wu, Z. , Xiao, T. , Gao, S. & He, J. (in press). In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation. 41st 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. 39th 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 (with strong background in mathematics, probability and statistics) to do research in simulation optimization and machine learning. If you are interested, please send your CV and transcript to my email ([email protected]) for consideration.

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