麻豆精选

• Postgraduate Certificate Higher Education (PGCHE) with Distinction, University 麻豆精选, 2021
• PhD in Applied and Computational Mathematics, University of Edinburgh, 2016
• MSc in Mathematical Biology, University of Dundee, 2012
• BSc in Mathematics and Applied Mathematics, Zhejiang University of Technology, 2011

Xiaocheng Shang obtained his PhD in Applied and Computational Mathematics under the supervision of Professor Ben Leimkuhler from the University of Edinburgh in 2016. After postdoctoral positions at the University of Edinburgh, Brown University in the United States, and ETH Zurich in Switzerland, he joined the School of Mathematics at the University 麻豆精选 as a Lecturer in 2019 and has been promoted to an Associate Professor in 2023.

Semester 2

LC Probability and Statistics

LM Largescale Optimisation for Machine Learning

Xiaocheng is interested in working with highly motivated students who share any of his research interests. Please get in touch via email.

Research themes

• Numerical Methods and Error Analysis for Stochastic Differential Equations
• Geometric Numerical Integration, Structure-Preserving Integrators
• Molecular Dynamics, Statistical Mechanics, Multiscale Methods
• Momentum-Conserving Thermostats, Dissipative Particle Dynamics
• Nonequilibrium Modelling, Polymer Melts, Adaptive Thermostats
• Bayesian Sampling, Data Science, Machine Learning of Potential Energy

Research activity

Xiaocheng's research has been addressing the sampling problem in a high dimensional space, i.e., the computation of averages with respect to a defined probability density that is a function of many variables. Such sampling problems arise in many application areas, including molecular dynamics, multiscale models, and Bayesian sampling techniques used in emerging machine learning applications. In particular, Xiaocheng explores theory, algorithms, and numerous applications of thermostat techniques, in the setting of a stochastic-dynamical system, that preserve the canonical Gibbs ensemble defined by an exponentiated energy function. More recently, Xiaocheng has started working on the construction of structure-preserving integrators for dissipative systems.

Xiaocheng's goal is to bring together the tools of numerical analysis and probability theory with the powerful principles underpinning multiscale modelling in materials science and engineering.