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Dr. GAO Siyang (高思陽博士)

BS(PKU), PhD(Univ of Wisconsin)

Assistant Professor

Contact Information

Office:  AC1-P6605
Phone: 34424759
Email: siyangao@cityu.edu.hk

Research Interests

  • simulation optimization
  • large-scale optimization
  • radiation treatment planning
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 methodology of large-scale optimization, computing budget allocation in simulation optimization and radiation treatment in health care.


Publication Show All Publications Show Prominent Publications


Journal

  • Gao, F. & Gao, S. (in press). A new strategy for selecting good enough designs using optimal computing budget allocation. Journal of Simulation.
  • Xiao, H. & Gao, S. (in press). Simulation budget allocation for selecting the top-m designs with input uncertainty. IEEE Transactions on Automatic Control.
  • Xiao, H. & Gao, S. (in press). Simulation budget allocation for simultaneously selecting the best and worst subsets. Automatica.
  • Gao, S. , Meyer, R. , D'Souza, W. , Shi, L. & Zhang, H. (2017). A machine-learning-based nested partitions framework for angle selection in radiotherapy. Optimization Methods and Software. 31. 1169 - 1188.
  • 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. , Lee, L. H. , Chen, C.-H. & Shi, L. (2017). A sequential budget allocation framework for simulation optimization. IEEE Transactions on Automation Science and Engineering. 14. 1185 - 1194.
  • 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.
  • Guo, H. , Gao, S. , Tsui, K. L. & Niu, T. (2017). Simulation optimization for medical staff configuration at emergency department in Hong Kong. IEEE Transactions on Automation Science and Engineering. 14. 1655 - 1665.
  • 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.
  • Chen, W. , Gao, S. , Chen, C.-H. & Shi, L. (2014). An optimal sample allocation strategy for partition-based random search. IEEE Transactions on Automation Science and Engineering. 11(1). 177 - 186.
  • Zhang, H. , Gao, S. , Chen, W. , Shi, L. , D'Souza, W. D. & Meyer, R. (2013). A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning. Physics in Medicine and Biology. 58. 1933 - 1946.
  • Gao, S. , Zhang, J. , Fu, Q. & Liu, F. (2009). Mechanical principles for stiffness design of the fibrous composite and its application. Acta Aeronautica et Astronautica Sinica. 30. 1227 - 1235.

Conference Paper

  • Gao, S. & Chen, W. (2016). Feasibility determination in presence of multiple performance measure constraints. Proceedings of the 2016 IEEE International Conference on Industrial Technology. (pp. 988 - 992). Taipei. Taiwan: .
  • Zhang, S. , Xu, J. , Huang, E. , Chen, C.-H. & Gao, S. (2016). Improving ordinal transformation through optimal combination of multi-model predictions. Proceedings of the 2016 IEEE International Conference on Industrial Technology. (pp. 1545 - 1549). Taipei. Taiwan: .
  • Gao, F. & Gao, S. (2016). Optimal computing budget allocation with exponential underlying distribution. Proceedings of the 2016 Winter Simulation Conference. (pp. 682 - 689). Arlington. USA: .
  • Gao, S. , Xiao, H. , Zhou, E. & Chen, W. (2016). Optimal computing budget allocation with input uncertainty. Proceedings of the 2016 Winter Simulation Conference. (pp. 839 - 846). Arlington. USA: .
  • Gao, S. & Chen, W. (2015). A note on the subset selection for simulation optimization. Proceedings of the 2015 Winter Simulation Conference. (pp. 3768 - 3776). Huntington Beach, CA. USA: .
  • Gao, S. & Shi, L. (2014). An optimal opportunity cost selection procedure for a fixed number of designs. Proceedings of the 2014 Winter Simulation Conference. (pp. 3952 - 3958). Savannah, GA. USA: .
  • Gao, S. , Zhang, X. & Shi, L. (2013). Evaluation of improvement probability for IMRT plans. Proceedings of the 2013 IEEE International Conference on Automation Science and Engineering. (pp. 474 - 479). Madison, WI. USA: .


For prospective students

  • I am looking for qualified Ph.D. students to do research on simulation optimization or large-scale optimization. If you are interested, please send your CV and transcript to my email (siyangao@cityu.edu.hk) for consideration.


Last update date : 08 Feb 2017