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Assistant Professor

Martin Ma

Assistant Professor
Member, SIT Emerging Technologies Initiative (SETI)
PhD in Petroleum Engineering, University of Alberta, Canada
Cluster:
Food, Chemical and Biotechnology
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Biography

Dr. Martin Ma (martin.ma@singaporetech.edu.sg) is an Assistant Professor at the Singapore Institute of Technology (SIT). Prior to joining SIT, he was a Research Scientist at New Mexico Tech and a Staff Scientist at Los Alamos National Laboratory. He earned his Ph.D. in Petroleum Engineering from the University of Alberta (Canada) in 2018, followed by postdoctoral appointments at the University of Alberta, Stanford University, and Los Alamos National Laboratory.

Over the past 14 years, Dr. Ma has applied advanced computational methods, optimization algorithms, machine learning models, numerical simulation, and software development to address challenging problems in science and engineering. His research spans energy production process optimization, decarbonization and clean energy generation, carbon capture, utilization, and storage (CCUS), and environmental risk mitigation.

He has served as principal investigator or co-principal investigator on numerous projects in Canada and the United States. His portfolio includes U.S. Department of Energy sponsored efforts such as the SimCCS Development and Applications program, the Science Informed Machine Learning for Accelerating Real Time Decisions in Subsurface Applications (SMART) initiative, and the Consortium Advancing Technology for Assessment of Lost Oil & Gas Wells (CATALOG) program. As co PI, he helped enhance the SimCCS tool for large scale CCUS infrastructure simulations, advancing the United States’ leadership in carbon storage modeling.

Dr. Ma’s innovations in modeling and simulation have earned several honors, including Los Alamos National Laboratory’s SPOT Award. To date, he has published more than 34 peer reviewed journal articles and 26 conference papers, and has presented widely at international conferences and workshops. He has delivered invited talks and lectures at venues including the AGU Fall Meeting, The University of Texas at Austin, and Penn State University, and has mentored three postdoctoral researchers and more than ten graduate and undergraduate students. 

In addition to his research and mentorship, Dr. Ma plays an active leadership role in the international CCUS community. He is helping to organize and lead major conferences, serving on the program committees for CCUS 2025 and CCUS 2026, where he contributes to shaping the scientific agenda and fostering collaboration among global experts in CCUS.

SIT Appointments

  • Member, SIT Emerging Technologies Initiative (SETI)
    – Present
  • Assistant Professor
    – Present

Education

  • PhD in Petroleum Engineering
    University of Alberta , Canada
  • Msc in Earth Science & Engineering
    King Abdullah University of Science and Technology , Saudi Arabia
  • BS in Petroleum Engineering
    China University of Petroleum (East China) , China

Professional Memberships

  • American Geophysical Union
    – Present
  • Society of Petroleum Engineers
    – Present

Corporate Experience

  • Adjunt Faculty in Petroleum and Natural Gas Engineering, New Mexico Tech
    Present
  • Research Scientist, New Mexico Tech
    –
  • Staff Scientist, Los Alamos National Laboratory
    –
  • Postdoctoral Research Associate, Los Alamos National Laboratory
    –
  • NSERC Postdoctoral Research Fellow, Stanford University
    –
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Research Interests

  • Machine learning
  • Optimization
  • Process control
  • Clean energy production and storage
  • Carbon capture, utilization, and storage (CCUS)

Current Projects

  • Ai-Assisted Integrated Verification & Tracing System for Rebar Delivery
    – Present
    • Co-PI
    • Funded by Industry Alignment Fund – Industry Collaboration Projects (IAF–ICP)
  • The Southwest Carbon, Capture, Utilization, and Storage Training and Research Partnership
    – Present
    • Co-PI
    • Funded by the US Department of Energy

Past Projects

  • CUSP: Four Corners Regional Initiative
    –
    • Co-I
    • Funded by the US Department of Energy
  • Four Corners Storage Project CarbonSAFE Phase III
    –
    • Co-I
    • Funded by the US Department of Energy
  • Power Sector CO2 Pipeline Analysis Using SimCCS
    –
    • Co-PI
    • Funded by the US Department of Energy
  • Consortium Advancing Technology for Assessment of Lost Oil & Gas Wells
    –
    • Task 7 lead
    • Funded by the US Department of Energy
  • SimCCS Development and Applications
    –
    • Co-PI
    • Funded by the US Department of Energy
  • Science-informed Machine Learning to Accelerate Real Time (SMART) Decisions in Subsurface Applications, Phase 2
    –
    • Co-I, main contributor for machine learning-based optimization and history matching
    • Funded by the US Department of Energy
  • National Scale CCS Pipeline Modeling
    –
    • Co-PI
    • Funded by the US Department of Energy
  • Development of Novel Machine Learning-Based Dynamic Proxy Frameworks for Production Optimization
    –
    • PI
    • Funded by Smart Fields at Stanford University
  • Development of an Innovative Fast Reservoir Simulation Tool using Big Data Analytics and Artificial Intelligence
    –
    • PI
    • Funded by Natural Sciences and Engineering Research Council of Canada
  • Reservoir Management and Advanced Optimization for Thermal And Thermal-Solvent Based Recovery Processes Using Fundamentals
    –
    • Co-I
    • Funded by the Canada First Research Excellence Fund (CFREF) via the Future Energy Systems
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Journal Papers

  • Ma, Z.*, Chen, B., and Pawar, R. Leveraging existing CO2 pipelines and pipeline rights-of-way for large-scale ccs deployment. Geoenergy Science and Engineering 255 (2025), 214063
    https://doi.org/10.1016/j.geoen.2025.214063

  • Ma, Z., Pachalieva, A. A., Sweeney, M. R., Chen, B., Viswanathan, H., and Hyman, J. D. Efficient approximations of effective permeability of fractured porous media using machine learning: A computational geometry approach. Mathematical Geosciences (2025), 1–41
    https://doi.org/10.1007/s11004-025-10197-2

  • Sayani, J. K. S., Wang, M., Ma, Z., Sharan, P., Mehana, M., and Chen, B. Techno-economic analysis of hydrogen transport via repurposed natural gas pipelines: Flow dynamics and infrastructure tradeoffs. International Journal of Hydrogen Energy 147 (2025), 150033
    https://doi.org/10.1016/j.ijhydene.2025.150033

  • Malki, M. L., Heimerl, J., Ma, Z., Chen, B., Van Wijk, J., and Mehana, M. Assessing the feasibility of retrofitting legacy wells for CO2 geological sequestration. International Journal of Greenhouse Gas Control 144 (2025), 104389
    https://doi.org/10.1016/j.ijggc.2025.104389

  • Zheng, F., Ma, Z.#, Viswanathan, H., Pawar, R., Jha, B., and Chen, B. Deep learning assisted multi-objective optimization of geological CO2 storage performance under geomechanical risks. SPE Journal (2025), 1–16 (First-authored by a supervised postdoctoral researcher)
    https://doi.org/10.2118/220850-PA

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Teaching Modules

Pharmaceutical Engineering, BEng (Hons)

  • PHE3019 - Process Safety

Chemical Engineering, BSc

  • TCE4011 - Integrated Work Study Programme
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