Theoretical Nuclear Physics and Machine Learning

My main area of research is involves finding ways machine learning can be incorporated into many-body calculations of nuclear systems. This includes creating methods that speed up traditional many-body calculations and developing machine learning workflows that allows for calculations in regions that have traditionally been impossible for many-body calculations due to computational prohibitions.

Computing in Physics Education

I am also interested in the best way computing and data science can be integrated into the existing undergraduate physics curriculum. Both programming and a general knowledge of data science skills (including machine learning) are becoming increasingly important in the types of fields physics majors go into (both graduate school and industry), but the traditional undergraduate curriculm includes little to no coverage of these topics.