Prof. WANG, Mengze
Presidential Assistant Professor
- Ph.D., Johns Hopkins University, 2022
- B.S., Peking University, 2016
Research Interests
- Data assimilation & Nonlinear optimization
- Transitional and turbulent flows
- Machine learning with limited data
- Extreme events in complex engineering systems
Biography
Prof. Mengze WANG is a Presidential Assistant Professor in the Department of Mechanical Engineering at City University of Hong Kong. Before joining CityUHK in 2026, he was a Postdoctoral Researcher at Massachusetts Institute of Technology, working with Prof. Themistoklis Sapsis and Prof. Raffaele Ferrari since 2023. He received his Ph.D. in Mechanical Engineering from Johns Hopkins University (2022) and B.Sc. in Theoretical and Applied Mechanics from Peking University (2016). His research focuses on developing data assimilation and machine learning methods for high-dimensional dynamical systems, with applications in aerospace engineering and geophysical flows. He has published over 10 articles on top journals in fluid mechanics and computational physics, and one book chapter. His honors include the Corrsin-Kovasznay Outstanding Paper Award and Andrea Prosperetti Travel Award from Johns Hopkins University.
Professional Experience
- 2026-present, Presidential Assistant Professor, Department of Mechanical Engineering, City University of Hong Kong
- 2023-2025, Postdoctoral Researcher, Department of Mechanical Engineering, Massachusetts Institute of Technology
- 2022-2023, Postdoctoral Researcher, Department of Mechanical Engineering, Johns Hopkins University
Honors and Awards
- Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University, 2024
- Andrea Prosperetti Travel Award, Johns Hopkins University, 2017
- Xu Zhilun Outstanding Student Award, Chinese Society of Theoretical and Applied Mechanics, 2016
- Outstanding Graduate, Peking University, 2016
- China National Scholarship, Peking University, 2013
Publications
- M. Wang, T. A. Zaki, Variational data assimilation in wall turbulence: From outer observations to wall stress and pressure. Journal of Fluid Mechanics, 1008 (2025): A26
- M. Wang, A. Souza, R. Ferrari, T. Sapsis, Spatially-resolved emulation of climate extremes via machine learning stochastic models. NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2023
- Y. Du, M. Wang, T. A. Zaki, State estimation in minimal turbulent channel flow: A comparative study of 4DVar and PINN. International Journal of Heat and Fluid Flow, 99 (2023): 109073
- M. Wang, T. A. Zaki, Synchronization of turbulence in channel flow. Journal of Fluid Mechanics, 943 (2022): A4
- Q. Wang, M. Wang, T. A. Zaki, What is observable from wall data in turbulent channel flow? Journal of Fluid Mechanics, 941 (2022): A48
- M. Wang, G. L. Eyink, T. A. Zaki, Origin of enhanced skin friction at the onset of boundary-layer transition. Journal of Fluid Mechanics, 941 (2022): A32
- T. A. Zaki, M. Wang, From limited observations to the state of turbulence: Fundamental difficulties of flow reconstruction. Physical Review Fluids, 6 (2021): 100501
- M. Wang, T. A. Zaki, State estimation in turbulent channel flow from limited observations. Journal of Fluid Mechanics, 917 (2021): A9
- Q. Li, C. Liu, H. Zhang, M. Wang, Z. Chen, Initiation and propagation of spherical premixed flames with inert solid particles. Combustion Theory and Modelling, 24.4 (2020): 606-631
- M. Wang, Q. Wang, T. A. Zaki, Discrete adjoint of fractional-step incompressible Navier-Stokes solver in curvilinear coordinates and application to data assimilation. Journal of Computational Physics, 396 (2019): 427–450
- D. Yu, C. Kong, J. Zhuo, Q. Yao, S. Li, M. Wang, Zhen-Yu Tian, Combustion characteristics of well-dispersed boron submicroparticles and plasma effect. Combustion and Flame, 188 (2018): 94-103
Book Publications
T. A. Zaki, M. Wang, Data assimilation and flow estimation. In Data driven Analysis and Modeling of Turbulent flows (2025): 129-181
Position Available
We always welcome applications for PhD, Research Assistant, Postdoctoral Research Fellow, and Visiting Scholar positions. Candidates with backgrounds in mechanics, aerospace engineering, applied math, machine learning, and relevant fields are particularly encouraged to apply. Feel free to send your CV to mz.wang@cityu.edu.hk if you are interested.