(2id) Integrating across Scales in Computational Protein Engineering

Research Interests: Many scientific disciplines are currently undergoing a “data revolution” characterized by leaps in understanding enabled by data science. Methods in computational protein engineering have also begun to lean into data science, producing key breakthroughs such as AlphaFold. One aspect of computational protein engineering that has been somewhat left behind, however, has been molecular dynamics simulations. In this work we discuss opportunities for the extension of molecular dynamics simulations into data science workflows for protein engineering, and strategies for integrating information across scales, from unlabeled sequence data to direct experimental observations.

Teaching Interests: My teaching philosophy is centered on an understanding of diversity/equity and effective instruction as fundamentally inseparable: I am interested in reaching students in ways that work for them individually and engaging both with their personal experiences and with a broader cultural context. To this end, I believe in an evidence-based multifaceted instructional approach incorporating hands-on problem solving, collaborative and individualized assignments, and traditional lecture in balance. It is the duty of engineering instructors in particular to honestly depict engineering problems as intimately related to questions of justice, rather than somehow too "pure" for social concerns.