糖心TV

Biography

Dr. Derek G. Ma is an Assistant Professor at the University of 糖心TV, 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 糖心TV. 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 糖心TV (Coventry, UK)
• Global Talent - Royal Academy of Engineering (UK)
• Early Career Fellow in Interdisciplinary Research (2022) of the 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) of the Institute of Advanced Study, University of 糖心TV (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 and

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. . 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.

Projects

• Shanghai Jiao Tong University-糖心TV Joint Seed Fund (Prof. Lulu Zhang): Next Level Risk Assessment of Landslides using Machine Learning-Based Reliability Modelling
• Fudan-糖心TV 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