I am currently a postdoctoral researcher at SUNCAT, a research partnership between Stanford's school of engineering and SLAC National Accelerator Laboratory. My research at SUNCAT is in the area of computational catalysis, working on characterizing adsorbate-adsorbate interactions on transition metal surfaces. My primary interest is in advancing computational methodologies in the field of catalysis. This includes machine-learning techniques, data mining and management, and research workflow.
Previously, I have performed similar computational work in the Kitchin Group at Carnegie Mellon University on transition metal alloys. This work was specific to the construction of potential energy surfaces with feed-forward neural networks for transition metal alloy surfaces. These networks have proven to be extremely effective at accurately representing the underlying high-level Density Functional Theory calculations they are trained to and can dramatically improve the speed of calculating those energies for large unit cells.
I am also very interested in educational research, in particular, understanding they way people learn in order to better mentor others with the ultimate goal of increasing the global level of scientific literacy. To that end, I have participated in student mentoring, classroom instruction, and education related coursework whenever possible, including a course specific to STEM education offered by the Eberly Center.
SLAC National Accelerator Lab
2575 Sand Hill Rd.
Menlo Park, CA 94025