Pyrcz Earns Promotion, Named IAMG Distinguished Lecturer
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June 13, 2023
Hildebrand Department of Petroleum and Geosystems Engineering (UT PGE) Associate Professor Michael Pyrcz — who has been promoted to full professor effective September 2023 — will be the 2024 International Association for Mathematical Geosciences (IAMG) Distinguished Lecturer.
IAMG annually recognizes one outstanding individual who demonstrates the ability to communicate mathematical concepts to a general geological audience, has a clear enthusiasm for mathematical geology, has achieved recognition for work in their field, and has established skill in working with individuals and in group discussions on geological problems. As IAMG distinguished lecturer, Dr. Pyrcz will prepare a series of talks on a variety of subjects in the mathematical geosciences to be presented in places where IAMG annual meetings are not normally held. He will interact and hold discussions with individuals, both professionals and students, on applications of mathematical geology to local problems of interest.
“IAMG has been important for my professional development as a scholar. I have been mentored and inspired by many of the former distinguished lecturers and am honored to be selected,” says Pyrcz. “I really appreciate the opportunity to reach out to communities that have not heard about the amazing opportunities for data science in subsurface engineering and geoscience.”
Pyrcz joined UT PGE in the summer of 2017 after 13 years in industry as a reservoir modeler, spatial data analytics research scientist, team leader and research program manager. He teaches and leads research in subsurface data analytics, geostatistics and machine learning as director of UT Austin’s Texas Center for Data Analytics and Geostatistics, and is the co-host of UT PGE’s annual Energy AI Hackathon with Associate Professor John Foster. In addition, Pyrcz shares all his university educational content online as the GeostatsGuy on YouTube, GitHub and Twitter.
Pyrcz’s current research is focused on improving subsurface resource characterization and modeling for enhanced development planning, minimized environmental impact, stronger profitability and better utilization of valuable natural resources. He and his students work on subsurface data-science-related problems, including new methods to learn from and reduce bias in subsurface data, improved data integration in subsurface models, and improved subsurface uncertainty models to support decision making.
