My research includes the development of software infrastructure for optimization and machine learning, and the application of this infrastructure to the modeling, simulation, and automated design of materials. I am particularly interested in design of experiments under uncertainty, the discovery of structure-property relationships, and the development of robust predictive analytics for nonlinear dynamical systems with missing or limited information.

I have a PhD in Physics from the University of Alabama at Birmingham, and began my career at Caltech in Materials Science and Advanced Computing. My background is in the design of electronic excited state materials using a union of experimental nonlinear optical spectroscopy, instrument simulation and modeling, and quantum chemical methods. I extended my work to vibrational materials and neutron scattering methods upon moving to Caltech in 2002, and to experimental time-resolved Raman scattering in 2006. I started working tightly with Los Alamos National Laboratory while developing a software framework for the Caltech PSAAP Center for the Predictive Modeling and Simulation of High-Energy Density Dynamic Response of Materials in 2008. I continued to work with LANL in the Exascale Co-Design Center for Materials in Extreme Environments in 2012. I began my research consulting career with a contract through the UQ Foundation to develop a software framework for the data analysis of nanostructure materials in 2013.

I have been a Staff Scientist at Los Alamos National Laboratory in Information Sciences since 2018, and specialize in developing software that utilizes artificial intelligence and machine learning in the optimization and predictive modeling of complex physical systems under uncertainty. I am Vice President and a co-founder of the UQ Foundation, where I have nearly a decade of consulting experience in the energy, financial, and technology markets. I am the primary author of over a dozen open source scientific software packages, where my software has been downloaded over three billion times.