Rich Pryor's Bios

Rich Pryor, a native of Pittsburgh (Hazelwood and Greenfield neighborhoods), graduated from Penn State University in 1965 with a degree in Physics and received a Ph.D. in Nuclear Physics from the University of Pittsburgh in 1970. He began his career at the Savannah River Laboratory in Aiken, SC, where he was assigned to the reactor physics group. His first task was to investigate the potential of Response Matrix Theory for reactor charge design. He successfully used this theory to compute the criticality of complex reactor lattices. He later applied this theory to the criticality of the entire reactor core. He was quickly promoted to a Research Supervisor, but left there in 1976 to work at Los Alamos National Laboratory on a new project called TRAC. TRAC was a state-of-the-art computer code to model loss-of-coolant accidents in nuclear power plants. The project was funded by the NRC and consisted of a team of about twenty-five staff members. After two years, he became the manager of the group and two years later was promoted to a Program Manager at the Laboratory where he managed many non-weapon nuclear programs.

In 1982, Rich was assigned to the Office of Science and Technology Policy in The White House where he served as a senior policy analysis for nuclear issues. He returned to Savannah River in 1984 as a Research Manager to start a new organization called Scientific Computing. While there, he was responsible for the purchase of the site's first CRAY supercomputer and he started many new programs in reactor safety and design, process modeling, computer graphics, and artificial intelligence. Rich went to Sandia National Laboratories in April 1989. He was promoted to a department manager and later to a senior scientist. His work at Sandia focused mainly on agent-based modeling and machine learning. This led to a patent on the use of software agents to model an economy. He retired from Sandia in December of 2004 to open an office in Albuquerque to do research on forecasting markets using evolutionary learning algorithms. He also serves as a consultant to Sandia.

Rich has computing experience in the areas of nuclear physics, neutron transport, two-phase fluid flow, neural networks, genetic algorithms, agent-based modeling, machine learning, and high performance parallel processing. He loves to write software and to develop new technologies. He has written many papers and has an article about some of his work in Business Week. As a member of the American Nuclear Society, he served as the Chairman of the Mathematics and Computation Division. He is also a member of the American Physical Society and the Civil Air Patrol where he is a mission pilot.

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Last updated: December 10, 2008