Dr. Jorge Muñoz is interested in developing and applying machine learning algorithms to extend the reach of atomistic simulations of materials to study the interplay between thermodynamics and kinetics. He is also interested in investigating the fundamental properties and mechanisms of systems capable of learning such as neural and Bayesian networks, particularly in cases that exhibit adaptation, emergence, or phase transitions.

Dr. Muñoz received his B.S. in Physics and Applied Mathematics from The University of Texas at El Paso (UTEP), his Ph.D. in Materials Science from the California Institute of Technology (Caltech), followed by employment at Intel Corporation as an Engineer in Components Research and as a Research Scientist in Algorithms Pathfinding.