Prof. ZHOU Xiang (周翔博士)

PhD(Princeton University)
B.Sc. (Peking University)

Associate Professor

Contact Information

Office:	LAU-16-277
Phone:	(+852) 3442-6421
Fax:	(+852) 3442-0515
Email:	xizhou@cityu.edu.hk
Web:	Researchgate

Research Interests

applied and computational mathematics
rare event in stochastic nonlinear dynamical systems
stochastic modelling, simulation, optimization, analysis and application to/from machine learning.

Prof. Xiang Zhou received his BSc from Peking University (School of Mathematical Sciences) and PhD from Princeton University (PACM). Before joining City University in 2012, he worked as a research associate at Princeton University and Brown University. His major research focus is the study of rare event and the development of new computational methods for stochastic models and machine learning algorithms. His research works include the transitions in stochastic dynamical systems, rare-event simulation, saddle-point calculations and high dimensional problems for controls. His research results have turned into peer-reviewed papers in SIAM journals, Journal of Computational Physics, Chaos, Nonlinearity and Annals of Applied Probability, etc.
He has a joint appointment at Department of Mathematics, College of Science, but recruits PhD students only via School of Data Science.

Previous Experience

May 2012 - Jun 2018, Assistant Professor, Department of Mathematics, City University of Hong Kong.

Publications Show All Publications Show Prominent Publications

Journal

(Nov 2022). Active Learning for Transition State Calculation. J. Sci. Comput. .
(Oct 2022). Value-Gradient based Formulation of Optimal Control Problem and Machine Learning Algorithm. SIAM J. Numer. Anal. .
(Jan 2022). Learn Quasi-Stationary Distributions of Finite State Markov Chain. Entropy. 24(1). 133 doi:10.3390/e24010133
(May 2020). Stochastic dynamics of an active particle escaping from a potential well. Chaos. 30. 053133 doi:10.1063/1.5140853
(June 2019). Quasi-potential calculation and minimum action method for limit cycle. Journal of Nonlinear Science. 29. 961 - 991. doi:10.1007/s00332-018-9509-3
(March 2016). Iterative minimization algorithm for efficient calculations of transition states. Journal of Computational Physics. 309. 69 - 87. doi:10.1016/j.jcp.2015.12.056
(January 2015). A cross-entropy scheme for mixtures. ACM Transactions on Modeling and Computer Simulation. 25. doi:10.1145/2685030
(June 2011). The gentlest ascent dynamics. Nonlinearity. 24. 1831 - 1842. doi:10.1088/0951-7715/24/6/008

Conference Paper

(Dec 2022). Residual-Quantile Adjustment for Adaptive Training of Physics-informed Neural Network. IEEE International Conference on Big Data.