Associate Professor

Department: Physics

Jorge Munoz is an Associate Professor of Physics at The University of Texas at El Paso (UTEP). His research group develops computational methodology, machine learning algorithms, and data pipelines. The group consists of computationally-inclined physicists, mathematicians, and engineers who work on understanding the structure, thermodynamics, and properties of ordinary and nuclear matter from as close as reasonably possible to quantum mechanics. The work is done on Department of Energy (DOE) and National Science Foundation (NSF) supercomputers.

Jorge received a PhD in Materials Physics from Caltech and a BS with a double major in Applied Mathematics and Physics from UTEP. He was a recipient of the Gates Millennium Scholarship and was named as a 2022 Cottrell Scholar the by Research Corporation for Science Advancement. He worked at Intel Corporation as an Engineer in Computer-Aided Design and as a Data Scientist in Algorithms Pathfinding. At Intel, he invented a machine learning algorithm based on the information entropy of mathematical graphs that was patented and has been influential.

Jorge has over 40 publications in peer-reviewed journals and has been granted 3 US patents. He has received research grants as a Principal Investigator at UTEP totaling more than 1 million dollars, and significant resources from Berkeley Lab in direct student support. He cares deeply about mentoring, and alumni of his group can now be found at many top academic institutions and tech companies. He is a faculty affiliate at Berkeley Lab, UTEP Applied AI Innovation Institute, and UTEP Computational Science Program.

Other Affiliations: Berkeley Lab (Center for Computational Science and Engineering)

Keywords of Expertise: Graph-Structured Data, Physics-Informed Machine Learning, Scientific Data Pipelines, Education And Labor Force Development.