神马福利影片

Biography

Dr. Derek G. Ma is an Assistant Professor at the 神马福利影片. Dr Ma鈥檚 research sits at the interface of AI, stochastic modelling, uncertainty quantification, and risk analytics, with applications across geosystems, natural hazards, and emerging energy and financial risk problems. His interdisciplinary work, recognised through a prestigious Early Career Fellowship, focuses on applying data science and physics-informed modelling to the analysis, prediction, and management of risk.

He was awarded Global Talent status by the Royal Academy of Engineering and has contributed to policy advisory work with the Welsh Government鈥檚 Coal Tips Safety Taskforces prior to taking up his permanent academic position at the 神马福利影片. He also serves as a corresponding member of ISSMGE TC309, the technical committee on Machine Learning and Big Data.

Dr Ma is recognised for his innovative contributions to stochastic computational modelling, uncertainty quantification, and risk analysis across both research and applied consulting contexts. His broader academic mission is to develop data-driven and physics-informed modelling tools that improve how uncertainty, risk, and resilience are understood, predicted, and managed in complex physical, environmental, and financial systems.

• Visiting Academic (2017) from University of Canterbury (Christchrch, New Zealand)
• Data Scientist Degree - Machine Learning (2021) from Udacity (United States)
• Ph.D. in Computational Geomechanics and Applied Probability (2021) from the School of Engineering, 神马福利影片 (Coventry, UK)
• Global Talent - Royal Academy of Engineering (UK)
• Early Career Fellow in Interdisciplinary Research (2022) of the Institute of Advanced Study, 神马福利影片 (Coventry, UK)
• Policy Advisor (2022), Coal Tips Safety Taskforce, Welsh Government (Wales, UK)
• Associate Fellow (2023) of the Institute of Advanced Study, 神马福利影片 (Coventry, UK)

Research Interests

Derek鈥檚 research focuses on AI-informed stochastic modelling, computational risk analysis and data-driven engineering. His work combines random fields, Bayesian inference, uncertainty quantification, computer vision, machine learning and numerical modelling to understand complex systems with strong spatial heterogeneity, randomness and risk.

Research portfolio includes:

• AI-informed Random Fields and Risk Assessment of Stochastic Systems
  • Developing data-driven and physics-informed stochastic modelling approaches to characterise spatial variability, uncertainty, and risk in complex engineering systems.
• Visual AI and Computer Vision for Geomaterials (e.g., Perception-based modelling)
  • Applying perception-based modelling, image processing, segmentation analysis, and computer vision techniques to study material behaviour.
• Energy Risk Pricing and Decision-Making
  • Exploring computational risk analytics, , , and emerging financial risk models for energy and climate-related decision-making.

Teaching Interests

Derek teaches and supervises across engineering mathematics, project-based learning and degree apprenticeship education. His teaching is strongly aligned with mathematical modelling, analytical reasoning, data-driven engineering and industry-facing engineering education.

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

Derek鈥檚 teaching philosophy is to help students build strong mathematical foundations, connect theory with engineering practice, and develop the confidence to apply analytical and computational methods to real-world engineering problems.

Selected Publications

• Liu, X., Li, X., Ma, G.*, and Rezania, M., 2025. . Computers and Geotechnics, 183, p.107184.
• Wang, Y., Ma, G.*, and Rezania, M., 2025. . Tunnelling and Underground Space Technology, 155, p.106202.
• Li, W., Ma, G., Jiang, M., Rezania, M. and Zhu, H., 2025. A. Computers and Geotechnics, 177, p.106868.
• Ma, G., Rezania, M., Nezhad, M.M. and Phoon, K.K., 2024. . Reliability Engineering & System Safety, p.110270.
• Liu, X., Ma, G.*, Rezania, M., Li, X., and Jiang, S. H. 2024. . Computers and Geotechnics, 174, 106641.
• Jiang, S., Li, J., Ma, G.*, and Rezania, M., 2024. . Computers and Geotechnics, 172, p.106413.
• 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 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., Mousavi Nezhad, M. and Shi, B., 2022. . Rock and Soil Mechanics, 43(7), pp.2003-2014.
• 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.
• Ma, G., Hu, X., Yin, Y., Luo, G. and Pan, Y., 2018. . 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, 神马福利影片
• Associate Fellow, Institute of Advanced Study, 神马福利影片
• 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