Ryan's academic background is in applied mathematics, with an emphasis on computational problems arising from engineering physics (e.g. numerical analysis, fluid mechanics, etc.). He earned his PhD in 2016 for his study of specific aspects of Boltzmann's equation describing a dilute gas of hard spheres (billiard balls), far from thermodynamic equilibrium. He has an ongoing interest in applications of statistical methodologies, especially Bayesian inference and uncertainty quantification, for machine learning applications in the health sector. Currently, he works as a software engineer for the academic unit of Biomedical Informatics and Data Science (BIDS) at the Yale School of Medicine, where he builds software systems which support various aspects of the unit's research mission.
Education & Training
- PhDNew York University, Mathematics (2016)
- BSCalifornia Institute of Technology, Applied and Computational Mathematics (2011)