Yijie Peng

I have been dedicated to methodology and theoretical research of stochastic simulation optimization of complex systems, and applied the new methods to financial engineering and risk management, artificial intelligence, health care and other fields. So far I have published nearly 20 academic papers in the top journals in the fields of operations research and management, control systems, including Operations Research, INFORMS Journal on Computing, IEEE Transactions on Automatic Control etc. I currently serve as an Associate Editor for the Asia-Pacific Journal of Operational Research and the IEEE Control Systems Society Conference Editorial Board. I was awarded the Best Conference Paper Award-Finalist from the IEEE Robotics and Automation Society in 2017 and the INFORMS Outstanding Simulation Publication Award in 2019. I am the principal investigator of five research grants including the Excellent Young Scholar Grant from National Science Foundation of China.

System simulation is a classic and traditional field. The advantage of simulation technology is that it can provide high-fidelity modeling for general complex systems, so it has drawn attention from both academia and industry. With the growth of technology and competition, companies have been focused on lean management, and simulation technology in management is becoming increasingly important. Complex management systems often include dynamics, and the simulation takes a long time; systems often include randomness, and accurate estimation of the average performance of the system requires to run a lot of simulation replications. Simulation in complex management system often contains control parameters that affect the system structure and discontinuous sample performance, which lead to difficulties for analyzing and optimizing the system performance. In the presence of numerous system-designs and high-dimensional parameters, the computational power of the computer often cannot meet the actual needs. Therefore, it is imperative to study how to effectively combine simulation and optimization from a theoretical and methodological perspective. New technologies and business environments have brought new opportunities and challenges to simulation optimization research. In the big data era, how to combine simulation model with data requires new methods and theories; new algorithms are needed to effectively solve high-dimensional optimization problems and improve robustness in artificial intelligence; with the advent of cloud technology and digital twin technology, the requirements on real-time information interaction and decision-making continue to increase in system design. My research focus on addressing theoretical and practical need on these issues.

Research Areas

• Simulation modeling and optimization
• Financial engineering and risk management
• Healthcare
• Artificial intelligence

Career Experience

• 2020.04- Assistant Professor, Guanghua School of Management, Peking University
• 2017.09-2020.04 Assistant Professor, College of Engineering, Peking University
• 2016.03-2017.08 Research Assistant Professor, Dept. Systems Engineering & Operations Research, George Mason University
• 2015.09-2016.03 Postdoctoral Scholar, R.H. Smith Business School, University of Maryland
• 2014.07-2015.08 Postdoctoral Scholar, School of Management, Fudan University
• 2012.08-2013.08 Research Scholar, R.H. Smith Business School, University of Maryland

Journal Papers

• Peter Glynn, Yijie Peng, Michael C. Fu, and Jian-Qiang Hu, Computing sensitivity of distortion risk measure, INFORMS Journal on Computing, accepted.
• Haidong Li, Xiaoyun Xu, Yijie Peng, and Chun-Hung Chen, Efficient sampling for selecting important nodes in random network, IEEE Transactions on Automatic Control, accepted.
• Zhenyu, Cui, Michael C. Fu, Yijie Peng, Lingjiong Zhu, Optimal unbiased estimation for expected cumulative discounted cost, European Journal of Operations Research, 286 (2), 604-618, 2020.
• Yijie Peng, Michael C. Fu, Bernd Heidergott, and Henry Lam, Maximum likelihood estimation by Monte Carlo simulation: towards data-driven stochastic modeling, Operations Research, accepted.
• Yijie Peng, Chun‐Hung Chen, Michael C. Fu, Jian-Qiang Hu and Ilya O. Ryzhov, Efficient sampling allocation procedure for optimal quantile selection, INFORMS Journal on Computing, accepted.
• Yijie Peng, Jie Song, Jie Xu, and Edwin K. P. Chong, Stochastic control framework for determining feasible alternatives in sampling allocation, IEEE Transactions on Automatic Control, 65(6), 2647 - 2653, 2020.
• Peter W. Glynn, Lin Fan, Michael C. Fu, Jian-Qiang Hu, and Yijie Peng, Technical Note: central limit theorems for estimated functions at estimated points, Operations Research, accepted.
• Joost Berkhout, Bernd Heidergott, Henry Lam, and Yijie Peng, From data to stochastic modeling and decision making: what can we do better?, Asia-Pacific Journal of Operational Research, 36(6), 1940012, 2019.
• Zhenyu Cui, Michael C. Fu, Jian-Qiang Hu, Yanchu Liu, Yijie Peng, and Lingjiong Zhu, On the variance property of single-run unbiased stochastic derivative estimators, INFORMS Journal on Computing, 32 (2), 390-407, 2020.
• Yijie Peng, Edward Huang, Jie Xu, Zhongshun Shi, and Chun-Hung Chen, A coordinate optimization approach for concurrent design, IEEE Transactions on Automatic Control, 64 (7), 2913-2920, 2019.
• Yijie Peng, Jie Xu, Loo-Hay Lee, Jian-Qiang Hu, and Chun-Hung Chen, Efficient simulation sampling allocation using multifidelity models, IEEE Transactions on Automatic Control, 64 (8), 3156-3169, 2019.
• Yijie Peng, Chun-Hung Chen, Michael C. Fu, and Jian-Qiang Hu, Gradient-based myopic allocation policy: an efficient sampling procedure in a low-confidence scenario, IEEE Transactions on Automatic Control, 63(9), 3091-3097, 2018.
• Yijie Peng, Edwin K.P. Chong, Chun-Hung Chen, and Michael C. Fu, Ranking and selection as stochastic control, IEEE Transactions on Automatic Control, 63 (8), 2359-2373, 2018.
• Yijie Peng, Michael C. Fu, Jian-Qiang Hu and Bernd Heidergott, A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters, Operations Research, 66 (2), 487-499, 2018. (INFORMS Simulation Society Outstanding Simulation Publication Award)
• Lei Lei, Yijie Peng, Michael C. Fu and Jian-Qiang Hu, Applications of the generalized likelihood ratio method to distribution sensitivity and steady-state simulation, Journal of Discrete Event Dynamic Systems, 28 (1), 109-125, 2018.
• Yijie Peng and Michael C. Fu, Myopic allocation policy with asymptotically optimal sampling rate, IEEE Transactions on Automatic Control, 62 (4), 2041-2047, 2017.
• Yijie Peng, Michael C. Fu and Jian-Qiang Hu, Gradient‐based simulated maximum likelihood estimation on stochastic volatility models using characteristic functions, Quantitative Finance, 16 (9), 1393-1411, 2016.
• Yijie Peng, Chun-Hung Chen, Michael C. Fu and J.Q. Hu, Dynamic sampling allocation and design selection, INFORMS Journal on Computing, 28 (2), 195-208, 2016.
• Yijie Peng, Michael C. Fu and J.Q. Hu, Gradient‐based simulated maximum likelihood estimation on Levy-driven Ornstein‐Uhlenbeck stochastic volatility models, Quantitative Finance, 14 (8), 1399-1414, 2014.
• Yijie Peng, Chun‐Hung Chen, Michael C. Fu and J.Q. Hu, Efficient simulation resource sharing and allocation for selecting the best, IEEE Transactions on Automatic Control, 58 (4), 1017 – 1023, 2013.
• Rachel Chen, Jianqiang Hu and Yijie Peng, Simulation of Levy-driven models and its application in finance, Numerical Algebra, Control and Optimization, 2(4), 749-765, 2012.

Conference Papers

• Xiangyu Yang, Jiaqiao Hu, Jian-Qiang Hu, and Yijie Peng, Asynchronous value iteration for Markov decision process with continuous state space, Proceedings of Winter Simulation Conference, 2020.
• Haidong Li, Henry Lam, Zhe Liang, and Yijie Peng, Context-dependent simulation optimization, Proceedings of Winter Simulation Conference, 2020.
• Lei Lei, Christos Alexopoulos, Yijie Peng, and James Wilson, Confidence intervals and confidence regions for quantiles based on conditional Monte Carlo and generalized likelihood ratios'', Proceedings of Winter Simulation Conference, 2020.
• Li Xiao, Yijie Peng, Jeff Hong, Zewu Ke, and Shuhuai Yang, Training artificial neural networks by generalized likelihood ratio method, Proceedings of IEEE Conference on Automation Science and Engineering, 2020.
  
• Gongbo Zhang, Chun-Hung Chen, Qing-shan Jia, and Yijie Peng, Dynamic sampling allocation for selecting a good enough alternative, Proceedings of IEEE Conference on Automation Science and Engineering, 2020.
• Yijie Peng, Michael C. Fu, Jian-Qiang Hu, and Lei Lei, Estimating quantile sensitivity for fianncial models with correlations and jumps, Proceedings of Winter Simulation Conference, 2019.
• Haidong Li, Yijie Peng, Xiaoyun Xu, Chun-Hung Chen, and Bernd Heidergott, Dynamic sampling procedure for decomposable random networks, Proceedings of Winter Simulation Conference, 2019.
• Bowen Pang, Xiaolei Xie, Bernd Heidergott, and Yijie Peng, Optimizing outpatient department staffing level using multi-fidelity models, Proceedings of IEEE Conference on Automation Science and Engineering, 2019.
• Nanne Dieleman, Bernd Heidergott, and Yijie Peng, Data-driven fitting of the G/G/1 queue, Proceedings of International Conference on Service Systems and Service Management, 2019.( Best Student Paper Award)
• Yijie Peng, Chun-Hung Chen, Edwin K.P. Chong, and Michael C. Fu, A review of static and dynamic optimization for ranking and selection, Proceedings of Winter Simulation Conference, 2018.
• Yijie Peng, Jie Song, Jie Xu, and Edwin K. P. Chong, Dynamic sampling allocation for feasibility determination, Proceedings of IEEE Conference on Automation Science and Engineering, 2018.
• Haidong Li, Xiaoyun Xu, Yijie Peng, and Chun-Hung Chen, Efficient sampling procedure for selecting the largest stationary probability of a Markov chain, Proceedings of IEEE Conference on Automation Science and Engineering, 2018.
• Yijie Peng, Michael C. Fu, Peter W. Glynn, and Jian-Qiang Hu, On the asymptotic analysis of quantile sensitivity estimation by Monte Carlo simulation, Proceedings of Winter Simulation Conference, 2017.
• Yijie Peng, Edward Huang, Jie Xu, and Chun-Hung Chen, Concurrent engineering: an optimization approach for team coordination through information sharing, Proceedings of IEEE Conference on Automation Science and Engineering, 2017. (Best Paper Finalist)
• Yijie Peng, Michael C. Fu and Jian-Qiang Hu, On the regularity conditions and applications for generalized likelihood ratio method, Proceedings of Winter Simulation Conference, 2016.
• Yijie Peng, Michael C. Fu, and Jian-Qiang Hu, Estimating distribution sensitivity using generalized likelihood ratio method, Proceedings of IEEE International Workshop on Discrete Event Systems, 2016.
• Yijie Peng, Chun-Hung Chen, Michael C. Fu, and Jian-Qiang Hu, Non-monotonicity of probability of correct selection, Proceedings of Winter Simulation Conference, 2015.
• Yijie Peng, Chun‐Hung Chen, Michael C. Fu and J.Q. Hu, A dynamic framework for statistical selection problems, Proceedings of Winter Simulation Conference, 2013.