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Prof. Bingchang Wang | Control Theory | Outstanding Scientist Award

Professor at Shandong University, China.

Prof. Bingchang Wang is a distinguished researcher in control theory, mean field games, and multi-agent systems.  he is a Professor at Shandong University, China. His expertise spans reinforcement learning, Markov processes, and event-based control. He has held research positions in Canada, Australia, and Hong Kong, working with leading IEEE Fellows. Prof. Wang is a Senior Member of IEEE and serves as an Associate Editor for Mathematical Problems in Engineering. He has received prestigious awards, including the 2021 Best Theory Paper Award at the China Automation Congress. His research contributes significantly to intelligent control and optimization, with numerous publications in top-tier journals like IEEE Transactions on Automatic Control and Automatica.

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Scopus Profile

Education 🎓

Prof. Wang pursued his undergraduate studies in Mathematics and Applied Mathematics at Ludong University, China, earning his B.Sc. degree in 2005. He then completed his M.Sc. in Probability and Statistics at Central South University, China (2005-2008), under the supervision of Prof. Zhen-Ting Hou. His master’s thesis focused on the local asymptotics of Markov modulated random walks with heavy-tailed increments and their applications in complex systems.

In 2008, he commenced his Ph.D. in System Modeling and Control at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China, under the guidance of Prof. Ji-Feng Zhang. His Ph.D. thesis, titled “Mean Field Games and Control of Multi-Agent Systems in a Markov Environment”, laid the foundation for his future research in distributed control and large-scale optimization. Throughout his academic journey, Prof. Wang developed a strong foundation in mathematical modeling, probability theory, and stochastic control, which continues to influence his innovative work in multi-agent systems and reinforcement learning.

work Experience💼

Prof. Wang has extensive international research experience, working with renowned scholars in control theory and automation. He began his postdoctoral research in 2011 at the University of Alberta, Canada, under the mentorship of Prof. Tongwen Chen (IEEE Fellow). In 2012, he moved to Australia as a Research Academic at the University of Newcastle, collaborating with Prof. Minyue Fu (IEEE Fellow) on advanced topics in stochastic control and optimization.

Returning to China in 2013, he joined Shandong University as an Associate Professor, where he played a crucial role in advancing research in multi-agent systems and reinforcement learning. During this period, he also held visiting research positions at Carleton University, Canada (2014-2015), working with Prof. Minyi Huang, and at The Hong Kong Polytechnic University (2016-2017), where he collaborated with Prof. Jianhui Huang on stochastic control problems in finance and engineering.

Since September 2021, Prof. Wang has been a Full Professor at Shandong University, China, leading innovative research projects in mean field games, event-based control, and AI-driven decision-making. He is an active IEEE Senior Member, a Program Committee Member for several top conferences, and an Associate Editor for Mathematical Problems in Engineering.

Research Focus

Prof. Wang’s research centers on mean field games and control, reinforcement learning, multi-agent systems, and Markov processes. His work addresses critical challenges in large-scale system optimization, intelligent automation, and distributed decision-making. By integrating game theory, stochastic control, and AI-driven strategies, he develops novel frameworks for robotic automation, smart production systems, and financial engineering.

His contributions to event-based control and reinforcement learning enhance adaptive decision-making in uncertain environments, optimizing processes in autonomous systems, industrial control, and AI-driven policy-making. Prof. Wang’s innovative models in Markov processes and stochastic control have significant applications in economics, energy systems, and networked control systems, advancing the field of intelligent automation.

Awards & Honors🏆 

Prof. Wang’s contributions to control theory and system optimization have been recognized through numerous prestigious awards. In 2021, he received the Best Theory Paper Award at the China Automation Congress, highlighting his pioneering work in mean field social systems and large-scale optimization. He was also honored with the Best Poster Paper Award at the Chinese Control Conference in 2020 and was a Finalist for the Guanzhaozhi Prize in 2019, a prestigious award recognizing outstanding young researchers in control systems engineering.

In 2018, Prof. Wang was awarded the IEEE CSS Beijing Chapter Youth Author Prize, acknowledging his exceptional contributions to stochastic control and reinforcement learning. His early career achievements were further recognized in 2017 when he was nominated for the Youth Author Prize at the Asian Control Conference. These accolades underscore his global influence in intelligent control and mathematical optimization.

Conclusion✅

Dr. Bing-Chang Wang is an outstanding candidate for the Research for Outstanding Scientist Award. His deep expertise in control systems, mean field games, and reinforcement learning, coupled with his strong publication record, prestigious awards, and international experience, make him highly deserving of this recognition. Addressing interdisciplinary research opportunities, industry collaboration, and public outreach could further elevate his scientific impact.

Publications to Noted 📚

Mean field LQG social optimization: A reinforcement learning approach

Authors: Zhenhui Xu, Bingchang Wang, Tielong Shen

Year: 2025

Citations: 0

Journal: Automatica

An Online Q-Learning Method for Linear-Quadratic Nonzero-Sum Stochastic Differential Games with Completely Unknown Dynamics

Authors: Baoqiang Zhang, Bingchang Wang, Ying Cao

Year: 2024

Citations: 0

Journal: Journal of Systems Science and Complexity

Mean Field Social Control for Production Output Adjustment with Noisy Sticky Prices

Authors: Bingchang Wang, Minyi Huang

Year: 2024

Citations: 3

Journal: Dynamic Games and Applications

An online value iteration method for linear-quadratic mean field social control with unknown dynamics

Authors: Bingchang Wang, Shumei Li, Ying Cao

Year: [No source information available]

Citations: 4

Discrete-time indefinite linear-quadratic mean field games and control: The finite-population case

Authors: Yong Liang, Bingchang Wang, Huanshui Zhang

Year: 2024

Citations: 2

Journal: Automatica

Differentially Private Consensus for Second-Order Multiagent Systems With Quantized Communication

Authors: Wenjun Zhang, Bingchang Wang, Yong Liang

Year: 2024

Citations: 3

Journal: IEEE Transactions on Neural Networks and Learning Systems

Discrete-time indefinite mean field linear quadratic games with multiplicative noise

Authors: Xiao Ma, Bingchang Wang, Huanshui Zhang

Year: 2024

Citations: 1

Journal: Asian Journal of Control

Bingchang Wang | Control Theory | Outstanding Scientist Award

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