Dr Mingtao Xia PhD

School of Mathematics
Assistant Professor

Contact details

Email: m.xia@bham.ac.uk

Address: University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Mingtao Xia is an Assistant Professor in the School of Mathematics at the University of Birmingham. Dr Xia's research focuses on scientific machine learning, uncertainty quantification, stochastic differential equations, mathematical biology, and scientific computing.

Qualifications

PhD in Applied Mathematics, University of California, Los Angeles (UCLA), 2023
BS in Applied and Computational Mathematics, Peking University, School of Mathematics, 2019

Biography

Dr Xia received their PhD in Applied Mathematics from UCLA in 2023 and their BS in Applied and Computational Mathematics from Peking University, School of Mathematics, in 2019. Before joining University of Birmingham, Dr Xia was a tenure-track Assistant Professor in Mathematics at the University of Houston and a Courant Instructor at New York University from 2023 to 2025. Dr Xia's research interests include scientific machine learning, uncertainty quantification, stochastic differential equations, mathematical biology, and scientific computing.

Teaching

Dr Xia's teaching will be at JBJI, and they are currently teaching the M & R course for MS students.

Postgraduate supervision

Dr Xia welcomes enquiries from prospective PhD students interested in research at the intersection of artificial intelligence, machine learning, uncertainty quantication, and their applications in biology, healthcare, and environmental sciences.

Research

Dr Xia's research focuses on developing mathematical and computational methods for scientific machine learning and uncertainty quantification. Current work includes Wasserstein-distance-based methods for reconstructing stochastic differential equations and uncertain models from time-series data, including applications to noisy cellular dynamics. Dr Xia also studies mathematical and kinetic models of structured cell populations, adaptive spectral methods for equations on unbounded domains, and related problems in scientific computing.

Publications

Recent publications

Article

Xia, M & Shen, Q 2026, 'A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty', Machine Learning: Science and Technology, vol. 7, no. 3, 035001. https://doi.org/10.1088/2632-2153/ae5c59

Xia, M, Shen, Q, Maini, PK, Gaffney, EA & Mogilner, A 2026, 'A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems', Neural Networks, vol. 193, 107893. https://doi.org/10.1016/j.neunet.2025.107893

Deng, Y, Shao, S, Mogilner, A & Xia, M 2025, 'Adaptive hyperbolic-cross-space mapped Jacobi method on unbounded domains with applications to solving multidimensional spatiotemporal integrodifferential equations', Journal of Computational Physics, vol. 520, 113492. https://doi.org/10.1016/j.jcp.2024.113492

Zhang, J, Li, X, Guo, X, You, Z, Bottcher, L, Mogilner, A, Hoffman, A, Chou, T & Xia, M 2025, 'Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations', PLoS Computational Biology, vol. 21, no. 9, e1013462. https://doi.org/10.1371/journal.pcbi.1013462

Xia, M, Li, X, Shen, Q & Chou, T 2025, 'Squared Wasserstein-2 loss functions for efficient learning of stochastic differential equations', Machine Learning, vol. 114, no. 11, 255. https://doi.org/10.1007/s10994-025-06908-9

Xia, M, Li, X, Shen, Q & Chou, T 2024, 'An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks', Machine Learning: Science and Technology, vol. 5, no. 4, 045052. https://doi.org/10.1088/2632-2153/ad9379

Xia, M & Chou, T 2024, 'Kinetic theories of state- and generation-dependent cell populations', Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), vol. 110, no. 6, 064146. https://doi.org/10.1103/PhysRevE.110.064146

Xia, M, Li, X, Shen, Q & Chou, T 2024, 'Learning unbounded-domain spatiotemporal differential equations using adaptive spectral methods', Journal of Applied Mathematics and Computing, vol. 70, no. 5, pp. 4395-4421. https://doi.org/10.1007/s12190-024-02131-2

Xia, M, Li, X & Chou, T 2026, 'Overcompensation of transient and permanent death rate increases in age-structured models with cannibalistic interactions', Physica D - Nonlinear Phenomena, vol. 470, no. Part A, 134339. https://doi.org/10.1016/j.physd.2024.134339

Huang, Z, Shi, C, Velli, M, Sioulas, N, Panasenco, O, Bowen, T, Matteini, L, Xia, M, Shi, X, Huang, S, Huang, J & Casillas, L 2024, 'Solar Wind Structures from the Gaussianity of Magnetic Magnitude', The Astrophysical Journal Letters, vol. 973, no. 1, L26. https://doi.org/10.3847/2041-8213/ad72f1

Chou, T, Shao, S & Xia, M 2023, 'Adaptive Hermite spectral methods in unbounded domains', Applied Numerical Mathematics, vol. 183, pp. 201-220. https://doi.org/10.1016/j.apnum.2022.09.003

Sun, HZ, Zhao, J, Liu, X, Qiu, M, Shen, H, Guillas, S, Giorio, C, Staniaszek, Z, Yu, P, Wan, MWL, Chim, MM, van Daalen, KR, Li, Y, Liu, Z, Xia, M, Ke, S, Zhao, H, Wang, H, He, K, Liu, H, Guo, Y & Archibald, AT 2023, 'Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality', The Innovation, vol. 4, no. 6, 100517. https://doi.org/10.1016/j.xinn.2023.100517

Xia, M, Böttcher, L & Chou, T 2023, 'Spectrally adapted physics-informed neural networks for solving unbounded domain problems', Machine Learning: Science and Technology, vol. 4, no. 2, 025024. https://doi.org/10.1088/2632-2153/acd0a1

Xia, M, Böttcher, L & Chou, T 2022, 'Controlling Epidemics Through Optimal Allocation of Test Kits and Vaccine Doses Across Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1422-1436. https://doi.org/10.1109/TNSE.2022.3144624

Dessalles, R, Pan, Y, Xia, M, Maestrini, D, D'Orsogna, MR & Chou, T 2022, 'How Naive T-Cell Clone Counts Are Shaped By Heterogeneous Thymic Output and Homeostatic Proliferation', Frontiers in immunology, vol. 12, 735135. https://doi.org/10.3389/fimmu.2021.735135

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