Professor Michael J. Pyrcz, The University of Texas at Austin

Professor Michael J. Pyrcz, The University of Texas at AustinProfessor Michael J. Pyrcz, The University of Texas at AustinProfessor Michael J. Pyrcz, The University of Texas at Austin

Professor Michael J. Pyrcz, The University of Texas at Austin

Professor Michael J. Pyrcz, The University of Texas at AustinProfessor Michael J. Pyrcz, The University of Texas at AustinProfessor Michael J. Pyrcz, The University of Texas at Austin
  • My Story
  • My Research
  • My Publications
  • My Students
  • My Resources
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    • My Story
    • My Research
    • My Publications
    • My Students
    • My Resources
    • My News
    • My Advice
  • My Story
  • My Research
  • My Publications
  • My Students
  • My Resources
  • My News
  • My Advice

Professors stand on the shoulders of amazing graduate students. Here are the incredible graduate students on my team. Please contact me if you are looking for excellent interns and full-time hires.

Current Ph.D. Students

Dursun Dashdamirov, PH.D. student


Resource data analytics, geostatistics and machine learning.

Suin Choi, Ph.D. Student


Explainable subsurface modeling with physics and machine learning.

Qianqian Zhou, Ph.D. Student


3D subsurface deep learning surrogate models.

Dinghan Wang, Ph.D. student, cosupervised with Prof. John Foster and Prof. Yingda Lu


Robust unconventional resource spatial data analytics, geostatistics and machine learning workflows.

Maria Gonzalez, Ph.D. Student


Automated, semi-automatic formation evaluation with deep learning.

Yining Huang, Ph.D. student, cosupervised with Prof. Hewei Tang


Training subsurface deep learning models.

Ryan McGuigan, Ph.D. student, cosupervised with Prof. John Foster


Uncertainty modeling robustness for optimum subsurface resource development.

Muhammad Muneeb Akmal, Ph.D. student, cosupervised with Prof. Kamy Sephernoori


Physics-informed machine learning for subsurface forecast proxy models.

Aun Al Ghaithi, Ph.D. student


Novel uncertainty modeling workflows to support optimum subsurface resource development.

Nataly Chacon Buitrago, Ph.D. student


Improved subsurface heterogeneity modeling with rule-based and generative AI-based modeling.

Ahmed Merzoug, Ph.D. student


A critical evaluation of performance of generative AI for subsurface modeling.

Elnara Rustamzade, Ph.D. student, cosupervised with Prof. John Foster

  

Deep learning for enhanced subsurface mdoeling to support optimum decision making.

Misael Morales, Ph.D. student, cosupervised with Prof. Carlos Torres-Verdin

  

Deep learning for the integration of 4D seismic and fibre for enhanced subsurface resource modeling.

Completed Ph.D. Students

Jose Lius Hernandez Mejia, Ph.D.

Summer 2024


Title: Enhanced Subsurface Estimation and Uncertainty Modeling through Data Science and Engineering Physics

  

Summary: New geostatistics, spatial data analytics, machine learning methods and workflows for unconventional reservoirs.





    Current M.Sc. Students

    Alexander Ifenaike, M.Sc. Student


    Spatial Data Analytics and Machine Learning for Subsurface Resource Modeling

    Completed M.Sc. Students

    Eldar Sharafutdinov, M.Sc.   

    M.Sc. completed Fall 2024.


    Title:  Spatiotemporal Variation in Anthropogenic Methane Emissions in the Appalachian Basin 


    Spatiotemporal data analytics with spatial point processes. 

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