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

  1. 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
  2. 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
  3. 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

Sample Publications