JJ Ruby

I am currently the Senior Director of Research and Development for the Houston Astros of Major League Baseball (MLB) and also hold an appointment as a Visiting Assistant Professor in the Mechanical Engineering Department at the University of Rochester. Previously I was a Lawrence postdoctoral fellow at Lawrence Livermore National Laboratory (LLNL) and a postdoctoral fellow at the Center for Matter at Atomic Pressures (CMAP). I received a B.S. in Astrophysics and Planetary Science form Villanova University and an M.A. and PhD in Physics from the University of Rochester, where I conducted my graduate research at the Laboratory for Laser Energetics performing experiments on the Omega Laser system.

My research interests are best summarized by the categorization of “applied information theory” which, in practice, means attempting to best understand how to extract information from complex  data-sets to test our models of how nature works. My work exists at the  interface between experimental physics and theoretical physics mixed with a strong dose of computational statistics. My research record includes work in astrophysics, high-energy-density physics, and now has recently moved into dynamical systems. The common theme among all these  systems is working with complicated data, which require an appreciation for the mechanisms of measurement and the distributions of uncertainty associated with them and which require non-trivial models to interpret and understand. 

Historically methods of dedicated prediction and experimentation, where individual variables can be isolated and tested, were given preference in the physical sciences but modern experiments and theories require special care with how conclusions are drawn given measurements/observations. Embracing a global view of model selection and self-consistency within analysis opens pathways to gaining information about system that would otherwise be off limits to rigorous scientific experimentation. My interests are in pushing the boundaries of what we are currently capable of in a rigorous and scalable way to enable the next generation of scientific thought. The stage for this work has been set by many previous researchers in the mid-late 20th  century, but only within the last decade have the computational tools become sufficiently prevalent to make this kind of work the standard for  scientists everywhere.