Michael Pyrcz is a professor in the Department of Petroleum and Geosystems Engineering, and the Jackson School of Geosciences, The University of Texas at Austin, where he researches and teaches subsurface, spatial data analytics, geostatistics, and machine learning. Michael is also the principal investigator of the Energy Analytics, an associate editor for Computers and Geosciences, and a board member for Mathematical Geosciences. Michael has written over 60 peer-reviewed publications, a Python package for spatial data analytics, and co-authored a textbook on spatial data analytics, ‘Geostatistical Reservoir Modeling’. More about Michael’s work: www.michaelpyrcz.com.

Begüm Demir is a professor and the founder head of the Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, TU Berlin and the head of the Big Data Analytics for Earth Observation research group at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). She performs research in the field of processing and analysis of large-scale Earth observation data acquired by airborne and satellite-borne systems. She was awarded by the prestigious ‘2018 Early Career Award’ by the IEEE Geoscience and Remote Sensing Society for her research contributions in machine learning for information retrieval in remote sensing. In 2018, she received a Starting Grant from the European Research Council (ERC) for her project “BigEarth: Accurate and Scalable Processing of Big Data in Earth Observation”.

Georgia Papacharalampous is a civil engineer, PhD, MSc from the National Technical University of Athens (NTUA) and an early career scientist. Currently, she works as a principal investigator at the School of Rural, Surveying and Geoinformatics Engineering of NTUA. She has also done research or taught courses at five other institutes after her PhD. Her main research interests rotate around water resources, machine and statistical learning, spatial interpolation, forecasting and statistical post-processing with a focus on uncertainty estimation and probabilistic predictions.

Maziar Raissi is an assistant professor of applied mathematics at the University of Colorado Boulder, with a Ph.D. in Applied Mathematics & Statistics, and Scientific Computations from University of Maryland College His research lies at the intersection of Probabilistic Machine Learning, Deep Learning, and Data Driven Scientific Computing, in particular the design of learning machines that leverage the underlying physical laws and/or governing equations to extract patterns from high-dimensional data generated from experiments.

Ahmed H. Elsheikh is a distinguished researcher at the Institute of Geoenergy Engineering, Heriot-Watt University, UK. Leading research at the intersection of computational modeling, statistical analysis, and machine learning to pioneer efficient techniques for subsurface characterization, inverse modeling, and optimal fluid control. Notable work includes generative adversarial networks for geological modeling, reduced-order modeling using physical residual networks, and optimal fluid control using reinforcement learning techniques. Prof. Elsheikh earned his bachelor's degree from Al-Azhar University, Egypt, and his master's and PhD degrees from McMaster University, Canada.