Zejian Zhou, Ph.D.
Ph.D., Electrical Engineering, University of Nevada
M.Eng., Electrical Engineering, Stevens Institute of Technology
Zejian Zhou is an Assistant Professor in Electrical Engineering and Robotics. He received Ph.D. in Electrical Engineering from the University of Nevada, Reno. His research covers a broad spectrum of robotics decision-making topics, including multi-agent optimal decision-making, mean-field games, reinforcement learning, and game theory. He is also interested in neuromorphic computing systems, robot-assisted wireless networks, and low-cost machine learning methods.
Zhou Z., & Xu H.*,(2020) Decentralized Adaptive Optimal Control for Massive Multi-agent Systems Using Mean Field Game with Self-Organizing Neural Networks, IEEE Transactions on Cybernetics (in press)
Zhou, Z.*, Xiang, Y., Xu, H., Yi, Z., Shi, D., & Wang, Z. (2020), A Novel Transfer Learning based Intelligent Non-intrusive Load Monitoring with Limited Measurements. IEEE Transactions on Instrumentation and Measurement.
Zhou, Z.*, Xiang, Y., Xu, H., Wang, Y., Shi, D., & Wang, Z. (2020). Self-organizing probability neural network-based intelligent non-intrusive load monitoring with applications to low-cost residential measuring devices. Transactions of the Institute of Measurement and Control.
Zhou Z., & Xu H*, Decentralized Adaptive Optimal Tracking Control for Massive Multi-agent Systems: An Actor-Critic-Mass Algorithm, IEEE Transactions on Neural Networks and Learning Systems (in press)
Zhou Z.*, Xiang, Y., Xu, H., Wang, Y., & Shi, D. A Novel Spiking Deep Neural Network based Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid, Journal of Modern Power Systems and Clean Energy (in press)
Zhou Z., & Xu H.*, Decentralized Optimal Large Scale Multi-Player Pursuit-evasion Strategies: A Mean Field Game Approach with Reinforcement Learning, Neurocomputing (in press)