Since 2005, I have worked as a researcher in an applied mathematics and computer science research group at Sandia National Laboratories. I collaborate with incredible scientists from all disciplines and enjoy a great balance of fundamental algorithms research, software implementation, and its application to solve engineering and other science problems.

While I have always had acuity for mathematics and computer science, I decided to pursue graduate studies in applied mathematics to better understand practical uses of mathematics. A variety of coursework (e.g., modeling, numerical analysis, optimization, inverse problems, and statistics), seminars, and conferences, helped me appreciate the many possibilities. I learned that applied mathematics has many faces, ranging from nearly exclusively theoretical to purely practical, applying established algorithms and software to solve concrete problems. I primarily focused my research on modeling HIV infection and predicting patient outcomes (math biology). This revealed my interest in applying computational algorithms to solve problems at the intersection of disciplinary science and mathematics.

Seated in an optimization and uncertainty analysis group at SNL, I develop algorithms and interfaces to use massively parallel computing to solve design and analysis problems across the scientific disciplines. These algorithms are implemented in DAKOTA, an open-source suite of optimization, sensitivity analysis, and uncertainty quantification tools. I develop this tool in a team environment and have applied it to problems micro-electro-mechanical systems (miniature silicon-based actuators, switches, and machines) materials discovery.