Dr Derek (Guotao) Ma

Biography

Dr Derek G. Ma is an Assistant Professor at the University of Warwick. His research sits at the interface of applied mathematics, AI, stochastic modelling, uncertainty quantification, and risk analytics. His current programme connects three complementary research branches:

• AI-informed random fields and risk assessment – learning spatial uncertainty while preserving mathematical structure;
• Visual AI for geomaterials – translating images and physical evidence into interpretable models of material behaviour;
• Energy risk pricing and decision-making – turning uncertain evidence into robust, risk-aware decisions.

Across these branches, he develops data-driven and physics-informed methods for complex physical, environmental, and financial systems, with applications across geosystems, natural hazards, energy, and finance. This interdisciplinary work has been recognised through a prestigious Early Career Fellowship.

He was awarded Global Talent status by the Royal Academy of Engineering and contributed to policy advisory work with the Welsh Government’s Coal Tips Safety Taskforces before taking up his permanent academic position at the University of Warwick. He also serves as a corresponding member of ISSMGE TC309, the technical committee on Machine Learning and Big Data.

His broader academic mission is to turn uncertainty into actionable risk intelligence. Further details are available on the Personal Website.

• Visiting Academic (2017), University of Canterbury (Christchurch, New Zealand)
• Data Scientist Degree – Machine Learning (2021), Udacity (United States)
• PhD in Computational Geomechanics and Applied Probability (2021), School of Engineering, University of Warwick (Coventry, UK)
• Global Talent – Royal Academy of Engineering (UK)
• Early Career Fellow in Interdisciplinary Research (2022), Institute of Advanced Study, University of Warwick (Coventry, UK)
• Policy Advisor (2022), Coal Tips Safety Taskforce, Welsh Government (Wales, UK)
• Associate Fellow (2023), Institute of Advanced Study, University of Warwick (Coventry, UK)
• Visiting Professor (2025), Southern University of Science and Technology (Shenzhen, China)
• Visiting Professor (2026), Fudan University (Shanghai, China)

Research Interests

Derek works at the interface of applied mathematics, artificial intelligence, stochastic modelling, and decision science. His research asks how incomplete, heterogeneous, and spatially varying evidence can be converted into reliable, risk-aware engineering decisions.

AI-informed stochastic fields and uncertainty quantification

Random fields, Bayesian inverse methods, probabilistic modelling, and physics-informed machine learning for characterising spatial variability and propagating uncertainty through complex engineering systems.

Visual intelligence for engineering materials

Computer vision, image segmentation, and perception-based modelling to extract interpretable material features and connect visual evidence with physical behaviour.

Computational risk and decision analytics

Reliability analysis, catastrophe risk, risk pricing, and robust decision-making for natural hazards, energy, infrastructure, and emerging financial systems.

Methods: stochastic processes; random fields; Bayesian inference; uncertainty quantification; machine learning; computer vision; and numerical modelling.

Teaching Interests

Derek teaches and supervises across engineering mathematics, mathematical modelling, project-based learning, and degree apprenticeship education. His teaching helps students move from formulas to model-based reasoning, connect mathematical structure with engineering practice, and use analytical and computational methods with confidence.

Module leadership

• ES1A1 Engineering Mathematics CEDA – Module Leader
• ES1A8 Engineering Mathematics EMDA – Module Leader
• Maths Bridging Programme – Module Leader

Supervision, tutorials, and laboratories

• ES327 Individual Project – Project Supervisor for Year 3 and Year 4 students
• ES196 Statics and Structures – Laboratory and tutorial teaching
• ES1A4 Engineering Structures – Laboratory teaching
• Tutorials for Year 2–4 students
• Tutorials and academic support for CEDA degree apprentices

Previous contributions

• ES192 Engineering Design
• ES3B6 Geotechnical Engineering
• ES2G7 Design Surveying and Field Practice

Teaching approach: clear mathematical foundations, active problem-solving, structured practice, and meaningful links between theory, computation, and real-world engineering decisions.

Selected Publications

Selected work grouped by research theme. * Corresponding author.

Data-driven characterisation and Bayesian inference

• Liu, X., Li, X., Ma, G.*, and Rezania, M., 2025. Characterization of spatially varying soil properties using an innovative constraint seed method. Computers and Geotechnics, 183, p.107184.
• Li, W., Ma, G., Jiang, M., Rezania, M. and Zhu, H., 2025. An adversarial multi-source transfer learning method for the stability analysis of methane hydrate-bearing sediments. Computers and Geotechnics, 177, p.106868.
• Liu, X., Ma, G.*, Rezania, M., Li, X., and Jiang, S. H. 2024. An improved BUS approach for Bayesian inverse analysis of soil parameters incorporating extensive field data. Computers and Geotechnics, 174, 106641.

Stochastic modelling, uncertainty, and reliability

• Ma, G., Rezania, M., Nezhad, M.M. and Phoon, K.K., 2024. Multivariate copula-based framework for stochastic analysis of landslide runout distance. Reliability Engineering & System Safety, p.110270.
• Jiang, S., Li, J., Ma, G.*, and Rezania, M., 2024. Probabilistic assessment of 3D slope failures in spatially variable soils by cooperative stochastic material point method. Computers and Geotechnics, 172, p.106413.
• Ma, G., Rezania, M., Mousavi Nezhad, M. and Hu, X., 2022. Uncertainty quantification of landslide runout motion considering soil interdependent anisotropy and fabric orientation. Landslides, 19(5), pp.1231-1247.
• Ma, G., Rezania, M. and Nezhad, M.M., 2022. Effects of spatial autocorrelation structure for friction angle on the runout distance in heterogeneous sand collapse. Transportation Geotechnics, 33, p.100705.
• Ma, G., Rezania, M. and Nezhad, M.M., 2022. Stochastic assessment of landslide influence zone by material point method and generalized geotechnical random field theory. ASCE - International Journal of Geomechanics, 22(4), p.04022002.
• Ma, G., Rezania, M. and Nezhad, M.M., 2022. Probabilistic post-failure analysis of landslides using stochastic material point method with non-stationary random fields. In 20th International Conference on Soil Mechanics and Geotechnical Engineering (ICSMGE 2022). Sydney.

Geohazards and computational geomechanics

• Wang, Y., Ma, G.*, and Rezania, M., 2025. A granular anisotropic model of underground rockburst considering the effect of radial stresses. Tunnelling and Underground Space Technology, 155, p.106202.
• Jiang, S., Liu, X., Ma, G.*, Rezania, M., 2023. Stability analysis of heterogeneous infinite slopes under rainfall-infiltration by means of an improved Green-Ampt model. Canadian Geotechnical Journal.
• Xi, C., Hu, X., Ma, G.*, Rezania, M., Liu, B. and He, K., 2022. Predictive model of regional coseismic landslides' permanent displacement considering uncertainty. Landslides, 19(10), pp.2513-2534.
• Ma, G., Rezania, M., Mousavi Nezhad, M. and Shi, B., 2022. Post-failure analysis of landslides in spatially varying soil deposits using stochastic material point method. Rock and Soil Mechanics, 43(7), pp.2003-2014.
• Ma, G., Hu, X., Yin, Y., Luo, G. and Pan, Y., 2018. Failure mechanisms and development of catastrophic rockslides triggered by precipitation and open-pit mining in Emei, Sichuan, China. Landslides, 15(7), pp.1401-1414.

Selected Projects

• Shanghai Jiao Tong University-Warwick Joint Seed Fund (Prof. Lulu Zhang): Next Level Risk Assessment of Landslides using Machine Learning-Based Reliability Modelling
• Fudan-Warwick Joint Seed Fund (Prof. Jian Pu): AI-driven Deep Learning Prediction for Subsurface Characterisation Using Random Fields

Academic Recognition and Service

• Global Talent, Royal Academy of Engineering, UK
• Early Career Fellow, Institute of Advanced Study, University of Warwick
• Associate Fellow, Institute of Advanced Study, University of Warwick
• Policy Advisor, Welsh Government Coal Tips Safety Taskforce
• Corresponding Member, ISSMGE TC309: Machine Learning and Big Data in Geotechnics
• Nominee, School of Engineering Staff Award – Brilliant Newcomer 2024