Dr. GAO Siyang (高思阳博士) BS(PKU), PhD(Univ of Wisconsin)



Associate Professor


Contact Information Office: AC1-P6611
Phone: 34424759
Email: siyangao@cityu.edu.hk
Web: personal webpage

Research Interests Simulation modeling and optimization
Applied probability
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 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.




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 (siyangao@cityu.edu.hk) for consideration.



Links my CV



Last update date : 23 Aug 2021