Dr Derek (Guotao) Ma

Biography

Dr. Derek G. Ma is an Assistant Professor at the University of Warwick, specializes in Computational Modeling and Data Science. His acclaimed interdisciplinary research, meriting a prestigious Early Career Fellowship, focuses on the application of data science to geo-engineering. Honored with the Global Talent awarded by the Royal Academy of Engineering, Dr. Ma has significantly contributed to policy advisory roles with the Welsh Government's Coal Tips Safety Taskforces before moving to his permanent position in Warwick. His expertise further encompasses a key position as a corresponding member of the ISSMGE's TC309 technical committee, dedicated to Machine Learning and Big Data. Dr. Ma is recognized for his innovative research in geohazards, as well as his contributions to consulting and academia, particularly through pioneering work in stochastic computational catastrophe modeling and risk uncertainty quantification.

• Visiting Academic (2017) from University of Canterbury (Christchrch, New Zealand)
• Ph.D. in Computational Geomechanics and Applied Probability (2021) from the School of Engineering, University of Warwick (Coventry, UK)
• Global Talent - Royal Academy of Engineering (UK)
• Early Career Fellow in Interdisciplinary Research (2022) of the Institute of Advanced Study, University of Warwick (Coventry, UK)
• Policy Advisor (2022), Coal Tips Safety Taskforce, Welsh Government (Wales, UK)
• Associate Fellow (2023) of the Institute of Advanced Study, University of Warwick (Coventry, UK)

Research Interests

His research focuses on multivariate modelling/AI prediction/probabilistic analysis in engineering sciences, specifically for failure analysis and risk assessment of granular flows, avalanches, and natural hazards. Derek develops robust statistical numerical algorithms through the integration of Computational Statistics and Data Science to quantitatively evaluate the uncertainties of granular flows in heterogeneous materials that exhibit significant randomness.

Research portfolio includes:

• Stochastic Analysis of Geo-systems (e.g. Random Field, Bayesian Inference)
• Risk Assessment and Crisis Response of Natural/Anthropological Disasters
• Artificial Intelligence and Machine Learning (e.g. Deep Learning)
• Risk Pricing for catastrophe insuranceLink opens in a new window and catastrophe bondsLink opens in a new window

Teaching Interests

• ES1A1 Engineering Mathematics CEDA (Module Leader)
• ES1A8 Engineering Mathematics EMDA (Module Leader)
• Maths Bridging Programe (Module Leader)
• ES327 Individual Project (Project Supervisor)
• ES196 Statics and Structures (Lab)
• ES1A4 Engineering Structures (Lab)
• Tutorials Year 2-4 students
• Tutorials CEDA students

Previous Contributions:

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

Selected Publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 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., 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., 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.
• 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.

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

Honors & Awards

• Nominations School of Engineering Staff Award - Brilliant Newcomer - 2024