As part of my research, I've developed various software for computational modeling of relativistic heavy-ion collisions and quark-gluon plasma. Here are a few highlights:
A new model for the initial conditions of heavy-ion collisions. The name is an homage to Trento, Italy, where my fellow Duke student Scott Moreland and I formulated the idea (it also stands for Reduced Thickness Event-by-event Nuclear Topology).
Modern implementation of free streaming: a simple, common model for the brief period after the initial condition but before quark-gluon plasma formation (when hydrodynamics takes over).
My published journal articles. See also my Inspire page.
J. E. Bernhard, J.S. Moreland, S. A. Bass, J. Liu, U. Heinz
First Bayesian analysis using our initial condition model T_{R}ENTo. Presents a number of physical insights including the first systematic, quantitative estimate of the temperature-dependent shear viscosity of the quark-gluon plasma.
J. E. Bernhard et. al.
My first paper applying Bayesian methods to characterize the quark-gluon plasma.
I have presented my primary research project on Bayesian parameter estimation numerous times around the world (see my Speaker Deck for a complete list). These are the most recent:
Overview of my research and recent results for a broad audience of statisticians and nuclear physicists. This was a shared talk; Steffen Bass presented the introduction.
My poster for Quark Matter 2015, the preeminent conference in heavy-ion physics. Presents results from the above paper Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium.