Dr Xiaocheng Shang BSc MSc PhD PGCHE

Dr Xiaocheng Shang

School of Mathematics
Associate Professor in Mathematical Optimisation and Data Science

Contact details

Telephone
+44 (0) 121 414 6460
Email
x.shang.1@bham.ac.uk
Address
School of Mathematics
Watson Building
University 麻豆精选
Edgbaston
Birmingham
B15 2TT
UK

Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science. His primary research interests lie in the optimal design of numerical methods for stochastic differential equations with a strong emphasis on applications ranging from computational mathematics, statistics, physics, to data science. He is a member of the Optimisation and Numerical Analysis Group, the Statistics and Data Science Group, as well as the Institute for Data and AI. He was a Fellow of , the UK's national institute for data science and artificial intelligence. He was also an  as well as an . He has received funding for his research from the Engineering and Physical Sciences Research Council (EPSRC), from the Royal Society via an  and a , and from the  via the  initiative. He was also the winner of the  for 2024-25.

Qualifications

  • 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

Biography

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.

Teaching

Semester 2

LC Probability and Statistics

LM Largescale Optimisation for Machine Learning

Postgraduate supervision

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

PhD opportunities

Research

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.

Publications

Recent publications

Article

McGuinness, R, Herring, D, Wu, X, Almandi, M, Bhangu, D, Collinson, L, Shang, X & 膶ernis, E 2025, '', Early Intervention in Psychiatry, vol. 19, no. 2, e70015.

Duong, MH & Shang, X 2022, '', Journal of Computational Physics, vol. 464, 111332.

Gou, Y, Balling, J, De Sy, V, Herold, M, De Keersmaecker, W, Slagter, B, Mullissa, A, Shang, X & Reiche, J 2022, '', Environmental Research Letters, vol. 17, no. 4, 044044.

Shang, X 2021, '', SIAM Journal on Scientific Computing, vol. 43, no. 3, A1929鈥揂1949, pp. A1929-A1949.

Albano, A, le Guillou, E, Danze虂, A, Moulitsas, I, Sahputra, IH, Rahmat, A, Duque-Daza, CA, Shang, X, Ng, KC, Ariane, M & Alexiadis, A 2021, '', ChemEngineering, vol. 5, no. 2, 30.

Shang, X & 脰ttinger, HC 2020, '', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 476, no. 2234, 20190446.

Shang, X & Kr枚ger, M 2020, '', SIAM Review, vol. 62, no. 4, pp. 901-935.

Shang, X, Kr枚ger, M & Leimkuhler, B 2017, '', Soft Matter, vol. 13, no. 45, pp. 8565-8578.

Leimkuhler, B & Shang, X 2016, '', SIAM Journal on Scientific Computing, vol. 38, no. 2, pp. A712-A736.

Leimkuhler, B & Shang, X 2016, '', Journal of Computational Physics, vol. 324, pp. 174-193.

Leimkuhler, B & Shang, X 2015, '', Journal of Computational Physics, vol. 280, pp. 72-95.

Conference contribution

Shang, X, Zhu, Z, Leimkuhler, B & Storkey, AJ 2015, . in Advances in Neural Information Processing Systems 28 . pp. 37-45. <>

Preprint

McGuinness, R, Herring, D, Wu, X, Almandi, M, Bhangu, D, Collinson, L, Shang, X & 膶ernis, E 2023 '' PsyArXiv.