 Ph.D. in Mathematics, August 2007.Rensselaer Polytechnic Institute 
About my workMy research interests focus on two areas of computational sciences and physics: (1) large scale Markov Chain Monte Carlo (MCMC) simulations with applications to statistical mechanics, lattice gauge simulations, high altitude fluid simulations, climate simulations, risk measurement for finance, and other simulations requiring large quantities of random numbers in parallel and (2) physics of gravitational theory including quantum gravity, dark energy, and dark matter. I believe that as exaflop scale supercomputing and teraflop personal supercomputing both become a reality with the spread of innovations in massively parallel vector processors such as GPGPUs and multicore processors, large scale simulations will become increasingly common and necessary to assess risk, test predictions of physical theories, and test engineering designs. My particular interest in gravitational theory will benefit enormously because of the need to do large N-body simulations on a galactic or cosmological scale as well as to test new theories of quantum gravity in computationally intensive lattice simulations. One of my research goals is to test new hypotheses of my own in gravitational theory such as the effects of de Sitter and anti-de Sitter symmetries on Einstein's theories. A second goal is to create tools that bring high performance computing (HPC) to the individual physicist who, at the current state of the art, must share HPC resources with many others. A third is to help groups of physicists address problems at a much higher level of complexity and fidelity with exaflop computing. As an example of how my work might impact HPC, my current work for NASA involves random number generators for several types of new technologies such as GPGPUs and Intel MIC hardware which promises to be the next phase in HPC. Particle transport software such as Geant4 consumes vast quantities of random numbers to generate particle features from detections by, for example, the Alpha Magnetic Spectrometer (AMS) on the International Space Station which is a particle detector for cosmic rays designed to look for antimatter, dark matter, and strangelets. Satellite technology is insufficient to power the supercomputer the AMS requires, but new technologies could, potentially, reduce the power consumption so such a device could be placed on a satellite, freeing space on the ISS for other experiments. In order to meet the requirements of running Geant4 on new technologies, however, new random number generators (RNGs) need to be developed that generate high quality solutions. Our high quality random number generator project addresses this need by developing and testing different types of RNGs for the quality and quantity necessary to do HPC on these technologies. DissertationTrapped Slender Vortex Filaments in Statistical Equilibrium Successfully defended with no revisions on 14 May 2007 Journal Papers
Grants
Patents
Working Papers and Technical Reports
Talks
Currently Working On
Research Interests
Teaching
Links
Updated 17 April 2012. |