Dr. Evan Chanki Um is a computational geophysicist and data scientist at Lawrence Berkeley National Laboratory. He earned a B.S. in Earth and Environmental Sciences from Korea University. After completing his military service, he obtained his M.S. in Geological Engineering from the University of Wisconsin-Madison and earned a Ph.D. in Geophysics from Stanford University, with distinctions such as the inaugural computational geophysics fellowship and the Stanford Graduate Fellowship. His research has been highly numerical-modeling-driven and data-driven. He has over 20 years of experience in large-scale high-performance parallel computing using NERSC supercomputing facilities. His past and current research projects include marine electromagnetic geophysics, borehole geophysics, seismic full waveform inversion, and geophysical monitoring and characterization for various targets (e.g., geothermal reservoirs, geological storages, EOR, fault zones, fracture networks) and geophysical exploration related to oil&gas, hydrogen and critical mineral resources. He actively collaborates with electrical engineers and field experts, playing a key role in designing and implementing numerical modeling experiments to assess innovative geophysical instruments like fiber optic electromagnetic sensors and casing sources. Trained as a computational and mathematical engineer, Dr. Um analyzes and adapts machine learning and statistical learning algorithms for scientific and engineering applications. His recent work integrates traditional multiphysics data (electromagnetic, active and passive seismic, and gravity) with machine and statistical learning algorithms, providing real-time imaging of geological  storage and instantaneous quantification of imaging uncertainties.