Background

David M. Rogers joined the Scientific Computing team in the NCCS early January, 2020. He obtained his Ph.D. in Physical Chemistry from University of Cincinnati in 2009 where he worked with Prof. Thomas Beck on applying Bayes’ theorem to the free energy problem with applications to multiscale modeling of fluids and interface chemistry. After a Post-doctoral fellowship at Sandia National Labs working on modeling of water and ion conduction with Dr. Susan Rempe, he joined the USF Department of Chemistry as an Assistant Professor in 2013. Over that time, he has worked on theory and application of statistical mechanical methods in liquids, biomolecules, and quantum models.

His research interests include mathematical and computational theory and methods for multiscale modeling. Especially interesting open problems include applications to small nonequilibrium systems, dielectric friction, dispersion, hydration, and its role in nanoscale devices. David’s focus at ORNL is developing more powerful and general libraries and interfaces for modeling these systems at scale.

Education

2009
University of Cincinnati
Physical Chemistry
Doctor of Philosophy (Ph.D.)

R&D Activities Contributions

Exascale Computing Project – Interoperable Design of Extreme-scale Application Software (IDEAS) - The IDEAS Project is intent on improving scientific productivity by qualitatively changing scientific software developer productivity, enabling a fundamentally different attitude to creating and supporting…

Awards

2020 — Honorable Mention, 2020 Better Scientific Software Fellowship

2018 — Founding Member of Eta of Florida Phi Beta Kappa Chapter

2016 — Top Reviewer Award, J. Chemical Physics

2011 — R&D100 Award, Biomimetic Membranes for Water Purification

2009 — Hans H. Jaffé Award for Outstanding Scholarship in Physical Chemistry

2006 — DOE Computational Science Graduate Fellowship (CSGF)

2004 — Biochemistry Award, UC Department of Chemistry

2004 — Phi Beta Kappa

Publications

- For a list of publications, see https://www.predictivestatmech.org/wiki/Publications

Highlights