The Machine Learning Lab is hosting a series of talks that highlight the diverse applications of machine learning. ML+X seminars welcome faculty from across UT Austin whose work intersects with machine learning and are held every other Friday during the semester from 3-4 pm CT. These talks spark engaging conversation and collaboration. On Friday, May 7 at 3 pm CT we will be joined by Michael J. Pyrcz to for his talk titled "Data Analytics and Machine Learning for Subsurface Engineering and Geoscience."

Friday, May 7, 2021
3:00 PM – 4:00 PM CT
Virtual: Register at https://utexas.qualtrics.com/jfe/form/SV_dmY8HIG9pIrvp2K

Abstract
The subsurface resource industry has a long history of working with large, complicated geoscience and engineering datasets. The subsurface industry been working with ‘big data’ for decades! There is a growing toolbox of legacy and new emerging data-driven methods available that may offer improved efficiency and potentially new insights from vast and complicated subsurface datasets. This talk is an opportunity to link subsurface data analytics and machine learning to fundamental concepts from probability, statistics, geoscience and engineering and to provide an enthusiastic, but at times critical, perspective on what we in the subsurface may expect in the data-driven science revolution. 

Speaker Bio
Recently, Michael made the move to The University of Texas at Austin to accept the role of Associate Professor in the Department of Petroleum and Geosystems Engineering, Cockrell School of Engineering, and the Department of Geological Sciences, Jackson School of Geosciences, with an assignment in the Bureau of Economic Geology. At The University of Texas at Austin, Michael teaches and supervises research on subsurface data analytics, geostatistics and machine learning. In addition, Michael accepted the role of Principal Investigator for the College of Natural Sciences, The University of Texas at Austin, freshman research initiative in energy data analytics. Before joining The University of Texas at Austin, Michael conducted and lead research on reservoir data analytics and modeling for 13 years with Chevron’s Energy Technology Company. He became an enterprise-wide subject matter expert, advising and mentoring on workflow development and best practice. Michael has written over 50 peer-reviewed publications, an open source Python spatial data analytics package (GeostatsPy) and a geostatistics textbook with Oxford University Press. He is currently the associate director of the Center for Subsurface Engineering and the Environment, an associate editor with Computers and Geosciences, an editorial board member for Mathematical Geosciences and the Program Chair for the Petroleum Data Driven Analytics Technical Section (PD²A) for the Society of Petroleum Engineers International. For more information go to www.michaelpyrcz.com, see his course lectures at www.youtube.com/GeostatsGuyLectures, along with the demonstration numerical workflows at https://github.com/GeostatsGuy and contributions to outreach through social media at https://twitter.com/GeostatsGuy.

Contact us: ML-Lab@austin.utexas.edu