Dr. Pingping Zhu

Assistant Professor - Department of Computer Sciences and Electrical Engineering
WAEC 3205


Dr. Pingping Zhu is an assistant professor in the Department of Computer Science and Electrical Engineering at Marshall University. Prior to joining Marshall University, he was a Research Associate in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Before that, He worked as a Postdoctoral Associate in the Department of Mechanical Engineering and Materials Science at Duke University. Dr. Pingping Zhu received the Ph.D. and M.S. degrees in the Department of Electrical and Computer Engineering from the University of Florida in 2013 and 2010, respectively, and the M.S. degree from the Institute for Pattern Recognition and Artificial Intelligence and B.S. degree in Department of Electronics and Information Engineering in Huazhong University of Science and Technology, China, in 2008 and 2006, respectively. His research interests include signal processing, machine learning, artificial intelligence, intelligent control, and human behavior research.

Ph.D. University of Florida, 2013
M.Sc. University of Florida, 2010
M.Sc. Huazhong University of Science and Technology, China, 2008
B.Sc. Huazhong University of Science and Technology, China, 2006
Signal Processing, Machine Learning, Intelligent Control
Pingping Zhu, Chang Liu, Silvia Ferrari, “Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems,” IEEE Transactions on Control of Network Systems, submitted, arXiv preprint arXiv:2003.01891, 2020.
Pingping Zhu, Silvia Ferrari, Julian Morelli, Richard Linares, and Bryce Doerr, “Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps,” Sensors, 19(7), pp. 1524, 2019.
Julian Morelli, Pingping Zhu, Bryce Doerr, Richard Linares, and Silvia Ferrari, “Integrated Mapping and Path Planning for Very Large-Scale Robotic (VLSR) Systems,” International Conference on Robotics and Automation (ICRA), 2019.
Bryce Doerr, Richard Linares, Pingping Zhu, and Silvia Ferrari, “Random Finite Set Theory and Optimal Control of Large Collaborative Swarms,” Journal of Guidance, Control, and Dynamics, submitted, arXiv preprint arXiv:1810.00696, 2018.
Hongchuan Wei, Pingping Zhu, Miao Liu, Jonathan P. How, and Silvia Ferrari, “Automatic Pan-tilt Camera Control for Learning Dirichlet Process Gaussian Process (DPGP) Mixture Models of Multiple Moving Targets,” IEEE Transactions on Automatic Control, 2018
Shi Chang, Jason Isaacs, Bo Fu, Jaejeong Shin, Pingping Zhu, and Silvia Ferrari, “Confidence Level Estimation in Multi-target Classification Problems,” SPIE Defense + Commercial Sensing, 2018.
Pingping Zhu, Jason Isaacs, Bo Fu, Silvia Ferrari, “Deep Learning Feature Extraction for Target Recognition and Classification in Underwater Sonar Images,” IEEE Conference on Decision and Control (CDC), 2017.
Keith Rudd, Greg Foderaro, Pingping Zhu, Silvia Ferrari, “A Generalized Reduced Gradient Method for the Optimal Control of Very Large-Scale Robotic Systems,” IEEE Transactions on Robotics, 33 (5), 1226-1232, 2017.
Pingping Zhu, Julian Morelli, Silvia Ferrari, “Value Function Approximation for Multiscale Dynamical Systems,” IEEE Conference on Decision and Control (CDC), 2016
Hongchuan Wei, Wenjie Lu, Pingping Zhu, Silvia Ferrari, Miao Liu, Robert H. Klein, Shayegan Omiddshafiei, Jonathan P. How, “Information value in nonparametric Dirichlet-process Gaussian-process (DPGP) mixture models,” Automatica, 74, 360-368, 2016.
IEEE Member
IEEE Computational Intelligence Society (CIS) Member
Society of Photographic and Electronics Engineers (SPIE) Member
Associate Editor: Section Editor of Current Robotics Reports
Reviewer: Journal: IEEE Transaction on Signal Processing, IEEE Transaction on Neural Networks and Learning Systems, IEEE Transaction on Cybernetics, Digital Signal Processing, IET Image Processing, Neural Computing and Applications, Signal, Image and Video Processing (SIVP), Information Fusion, Conference: IEEE Conference on Decision and Control (CDC), IEEE International Joint Conference on Neural Networks (IJCNN), IEEE International Conference on Intelligent Robots and Systems (IROS)