Dr Xiaocheng Shang PhD

Dr Xiaocheng Shang

School of Mathematics
Lecturer in Mathematics and Statistics

Contact details

University of Birmingham
B15 2TT

Xiaocheng Shang is a Lecturer in Mathematics and Statistics. Xiaocheng’s 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. Xiaocheng is also a member of the Numerical Analysis Group. Xiaocheng has received funding for his research from the Royal Society via an International Exchanges Award

Please visit Xiaocheng’s personal webpage for more information.


Lecturer in Mathematics and Statistics

  • PhD in Applied and Computational Mathematics, University of Edinburgh, 2016
  • MSc in Mathematical Biology (First Class), University of Dundee, 2012
  • BSc in Mathematics and Applied Mathematics (First Class), 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 of Birmingham as a Lecturer in 2019.


Statistical Methods in Economics, Jinan University-University of Birmingham Joint Institute

Postgraduate supervision

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


Research Interests:

  • Numerical Methods and Error Analysis for Stochastic Differential Equations
  • Geometric Numerical Integration, Structure-Preserving Integrators, GENERIC
  • 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


Recent publications


Shang, X 2021, 'Accurate and efficient splitting methods for dissipative particle dynamics', SIAM Journal on Scientific Computing, vol. 43, no. 3, A1929–A1949. https://doi.org/10.1137/20M1336230

Albano, A, le Guillou, E, Danzé, A, Moulitsas, I, Sahputra, IH, Rahmat, A, Duque-Daza, CA, Shang, X, Ng, KC, Ariane, M & Alexiadis, A 2021, 'How to modify LAMMPS: from the prospective of a particle method researcher', ChemEngineering, vol. 5, no. 2, 30. https://doi.org/10.3390/chemengineering5020030

Shang, X & Öttinger, HC 2020, 'Structure-preserving integrators for dissipative systems based on reversible-irreversible splitting', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 476, no. 2234, 20190446. https://doi.org/10.1098/rspa.2019.0446

Shang, X & Kröger, M 2020, 'Time correlation functions of equilibrium and nonequilibrium Langevin dynamics: derivations and numerics using random numbers', SIAM Review, vol. 62, no. 4, pp. 901-935. https://doi.org/10.1137/19M1255471

Shang, X, Kröger, M & Leimkuhler, B 2017, 'Assessing numerical methods for molecular and particle simulation', Soft Matter, vol. 13, no. 45, pp. 8565-8578. https://doi.org/10.1039/C7SM01526G

Leimkuhler, B & Shang, X 2016, 'Adaptive thermostats for noisy gradient systems', SIAM Journal on Scientific Computing, vol. 38, no. 2, pp. A712-A736. https://doi.org/10.1137/15m102318x

Leimkuhler, B & Shang, X 2016, 'Pairwise adaptive thermostats for improved accuracy and stability in dissipative particle dynamics', Journal of Computational Physics, vol. 324, pp. 174-193. https://doi.org/10.1016/j.jcp.2016.07.034

Leimkuhler, B & Shang, X 2015, 'On the numerical treatment of dissipative particle dynamics and related systems', Journal of Computational Physics, vol. 280, pp. 72-95. https://doi.org/10.1016/j.jcp.2014.09.008

Conference contribution

Shang, X, Zhu, Z, Leimkuhler, B & Storkey, AJ 2015, Covariance-controlled adaptive Langevin thermostat for large-scale Bayesian sampling. in Advances in Neural Information Processing Systems 28 . pp. 37-45. <https://papers.nips.cc/paper/5978-covariance-controlled-adaptive-langevin-thermostat-for-large-scale-bayesian-sampling.pdf>

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