糖心TV

Biography

Dr Derek G. Ma is an Assistant Professor at the University of 糖心TV. 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鈥檚 Coal Tips Safety Taskforces before taking up his permanent academic position at the University of 糖心TV. 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 .

• 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 糖心TV (Coventry, UK)
• Global Talent – Royal Academy of Engineering (UK)
• Early Career Fellow in Interdisciplinary Research (2022), Institute of Advanced Study, University of 糖心TV (Coventry, UK)
• Policy Advisor (2022), Coal Tips Safety Taskforce, Welsh Government (Wales, UK)
• Associate Fellow (2023), Institute of Advanced Study, University of 糖心TV (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

• Li, J.-P., Jiang, S.-H., Wan, J.-H., Ma, G.-T., Rezania, M. and Meng, J., 2026. . International Journal for Numerical and Analytical Methods in Geomechanics. First published online 27 August 2026.
• Pan, Z., Guo, Z., Zhao, D., Zhu, Y., Liang, X., Yin, M., Li, H., Zeng, Z., Ma, G., Mousavi Nezhad, M. and Zheng, C., 2026. . Artificial Intelligence & Environment, 1(3), pp.189–202.
• Liu, X., Li, X., Ma, G.*, and Rezania, M., 2025. . Computers and Geotechnics, 183, p.107184.
• Li, W., Ma, G., Jiang, M., Rezania, M. and Zhu, H., 2025. . Computers and Geotechnics, 177, p.106868.
• Liu, X., Ma, G.*, Rezania, M., Li, X., and Jiang, S. H. 2024. . Computers and Geotechnics, 174, 106641.

Stochastic modelling, uncertainty, and reliability

• Ma, G., Rezania, M., Nezhad, M.M. and Phoon, K.K., 2024. . Reliability Engineering & System Safety, p.110270.
• Jiang, S., Li, J., Ma, G.*, and Rezania, M., 2024. . Computers and Geotechnics, 172, p.106413.
• Ma, G., Rezania, M., Mousavi Nezhad, M. and Hu, X., 2022. . Landslides, 19(5), pp.1231-1247.
• Ma, G., Rezania, M. and Nezhad, M.M., 2022. . Transportation Geotechnics, 33, p.100705.
• Ma, G., Rezania, M. and Nezhad, M.M., 2022. . ASCE - International Journal of Geomechanics, 22(4), p.04022002.
• Ma, G., Rezania, M. and Nezhad, M.M., 2022. . 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. . Tunnelling and Underground Space Technology, 155, p.106202.
• Jiang, S., Liu, X., Ma, G.*, Rezania, M., 2023. . Canadian Geotechnical Journal.
• Xi, C., Hu, X., Ma, G.*, Rezania, M., Liu, B. and He, K., 2022. . Landslides, 19(10), pp.2513-2534.
• Ma, G., Rezania, M., Mousavi Nezhad, M. and Shi, B., 2022. . Rock and Soil Mechanics, 43(7), pp.2003-2014.
• Ma, G., Hu, X., Yin, Y., Luo, G. and Pan, Y., 2018. . Landslides, 15(7), pp.1401-1414.

Selected Projects

• University of 糖心TV Institute of Advanced Study (IAS) – Interdisciplinary Research Development Award (2026): Integrated Risk Modelling for Decentralised Finance. Co-Investigator, with Dr Kaihua Qin (Principal Investigator, Department of Computer Science, University of 糖心TV) and Prof Jiahua Xu (University College London). This project integrates computer security, stochastic modelling, and financial economics to assess systemic risks and cascading failures in decentralised financial systems.

• Fudan–糖心TV Joint Seed Fund: AI-driven Deep Learning Prediction for Subsurface Characterisation Using Random Fields. Co-Investigator, with Dr Feng Mao (University of 糖心TV) and Prof Jian Pu (Fudan University). The project develops AI-driven approaches for subsurface characterisation, integrating deep learning and stochastic random field modelling to address spatial uncertainty in geotechnical engineering.

• Shanghai Jiao Tong University–糖心TV Joint Seed Fund: Next Level Risk Assessment of Landslides using Machine Learning-Based Reliability Modelling. In collaboration with Prof Lulu Zhang (Shanghai Jiao Tong University). This project combines machine learning and probabilistic reliability analysis to improve landslide risk assessment under geological and geotechnical uncertainties.

• European Commission Horizon 2020 – Marie Sk艂odowska-Curie Research and Innovation Staff Exchange (MSCA-RISE, 2020–2026): . Participating Researcher. Contributed to interdisciplinary research on stochastic modelling, spatial heterogeneity, and uncertainty quantification in environmental and subsurface systems.

• University of 糖心TV Institute of Advanced Study (IAS) – Early Career Fellowship (2021–2022): Early Career Fellow in Interdisciplinary Research. Fellowship Holder. Supported independent research development in computational geomechanics, stochastic modelling, and uncertainty quantification for landslide and geohazard risk assessment.

• Welsh Government – Coal Tip Safety Taskforce (2022): Coal Tip Landslip Modelling and Risk Assessment. Researcher and Policy Advisor. Applied computational geomechanics and landslide risk modelling to support evidence-based assessment of disused coal tips and the development of coal tip safety policy in Wales.

Academic Recognition and Service

• Global Talent, Royal Academy of Engineering, UK
• Early Career Fellow, Institute of Advanced Study, University of 糖心TV
• Associate Fellow, Institute of Advanced Study, University of 糖心TV
• 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