Research in High Performance Scientific Computing
Synopsis of the Research Area
High Performance Numerical Algorithms Applied to Science and Finance. An example of application to Physics is Lattice Quantum Chromodynamics. This is a numerical approach to the mathematical model that describes quarks, which are the basic constituents of 99.9% of known matter. Many particles once considered elementary have been proven to be comprised of different combinations of six types of quarks. The property of these particles are computed using Lattice QCD programs running on state of the art supercomputers and compared with results of the experiments.
An example of application to Finance is Quantitative Risk Management and in particular in the use of Markov Chain Monte Carlo to simulate systems subject to uncertainty and to pricing exotic financial derivatives.
Faculty Working in this Research Area
Prof. Massimo Di Pierro
Current Research Projects and Students
Visualization for Lattice QCD: This is a project supported by the Department of Energy that aims to develop a collection of tools for visualization of Lattice QCD data. Students: Yaoqian Zhong, Brian Schinazi
Pattern Derivatives: We proposed a new type of financial derivative called pattern derivative and we developed a computational model to price these derivatives. Students: Casey Schroeder
Semantic Web applications to Science: We created an extension to the web2py framework to expose database date as Linked Data for the semantic web and we are working on publishing data about elementary particles (masses, quantum numbers, branching ratios) using this technology. Students: Christopher Baron
M. Di Pierro, Y. Zhong, and B. Schinazi, “mc4qcd: Online Analysis Tool for Lattice QCD”, Proceedings of ACAT2010, Jaipur India, 2010.
T. Burch et al., “Quarkonium mass splittings in three-flavor lattice QCD”, Physical Review D 81.034508 (2010).
M. Di Pierro et al., “Visualization as a tool for understanding QCD evolution algorithms”, Proodeedings of Scidac 2009, Journal Physics Conference Series (2009).
- M. Di Pierro and J. Mosevich, “Effects of Skewness and Kurtosis on Portfolio Rankings”, Quantitative Finance, #TBD (2010).