Rate My Professor Huazhen Fang

Huazhen Fang served as an Associate Professor in the Department of Mechanical Engineering at the University of Kansas from August 2014, initially as Assistant Professor, until August 2025. He currently holds a Faculty Affiliate appointment at the University of Kansas and serves as Courtesy Associate Professor in the Department of Electrical Engineering and Computer Science. Fang earned his Ph.D. in Mechanical Engineering with a focus on Dynamic Systems and Control from the University of California, San Diego in 2014, his M.Sc. in Mechanical Engineering from the University of Saskatchewan in 2009, and his B.Eng. in Computer Science and Technology from Northwestern Polytechnical University in 2006. During his tenure at KU, he led the Information & Smart Systems Laboratory, maintained affiliations with the Institute for Information Sciences, and contributed to impactful research initiatives.

Fang's research specializations include systems and control, advanced battery management, energy storage systems, robotics, dynamics, estimation, learning, and applications in electrified transportation and vehicle systems. He has received major awards such as the University Scholarly Achievement Award in 2024 for outstanding contributions to advanced battery management, enhancing lithium-ion battery safety and performance for electric vehicles, aircraft, and grid energy storage through interdisciplinary work in systems and control, power electronics, machine learning, and electrochemistry. Additional honors include the National Science Foundation Faculty Early Career Development Award in 2019, Miller Scholar Awards in 2018, 2019, and 2023, Miller Professional Development Award in 2022, Wesley G. Cramer Mechanical Engineering Faculty Award in 2016, and Big XII Faculty Fellowship in 2015. Key publications feature "Advanced control in marine mechatronic systems: A survey" (IEEE/ASME Transactions on Mechatronics, 2017), "Nonlinear Bayesian estimation: from Kalman filtering to a broader horizon" (IEEE/CAA Journal of Automatica Sinica, 2018), "Integrating physics-based modeling with machine learning for lithium-ion batteries" (Applied Energy, 2023), "State of charge estimation for lithium-ion batteries: An adaptive approach" (Control Engineering Practice, 2014), and "Health-aware and user-involved battery charging management for electric vehicles: Linear quadratic strategies" (IEEE Transactions on Control Systems Technology, 2016). His funded research from NSF, Department of Energy, Army Research Laboratory, and others underscores his influence in the field.