My work in materials science and condensed matter physics. Much of my work is aimed at developing and using computational methods to model materials ranging from hard-core quantum mechanical properties of weird quantum materials to the behavior of micron-and nanoscale magnetic systems to microstructural evolution of super-alloys, but some of my research is more analytical or semi-analytical. More recently, I have to use aspects of machine learning in materials modeling, including active learning and various neural networks to speed up learning and design processes. In my present position at Seagate, I have been ramping up a high-performance computing framework for magnetic and materials modeling in addition to code development to enhance performance. Prior to leaving Argonne, I lead validation and verification work within the Center for Predictive Simulations of Functional Materials, a computational materials center focusing on developing and using quantum Monte Carlo methods for materials modeling, and I was Deputy Director for the Energy Frontier Research Center "Center for the Advancement of Topological Semimetals" until my departure from Argonne. At Argonne's Physical Sciences and Engineering directorate, I also lead efforts to develop strategies in artificial intelligence and machine learning. In my previous positions at Seagate over a decade ago I was responsible for materials design and roadmaps for spin transfer torque random access memories and resistive memories, and device design of advanced writers, including assisted-writing technologies, transducer design and modeling, and transducer technology roadmaps. I have broad and wide experience in research and development, ranging from fundamental theory, device physics, device design, modeling; analytical and numerical techniques.I am a Fellow of the American Physical Society and was in 2022 awarded an accolade as a Distinguished Associate by the Materials Measurement Laboratory at the National Institutes for Standards and Technology.