Speaker: Dr. Daniel Katz
Parallel Applications Technologies group, Modeling and Data Management Section,
Jet Propulsion Laboratory, California Institute of Technology
Title: Gaining Knowledge from Large Datasets using
Parallel Distributed Computing
Date and Time: Monday, April 11, 3:30 p.m.
Place:152 Coates Hall
Abstract:
JPL's Parallel Applications Technologies Group has been exploring the issues of
data access and visualization of very large datasets over the past 10 or so years.
This work has used a number of types of parallel and distributed computers. This talk
will highlight some of the applications and tools we have developed, including how they
use computing resources. Our applications focus on NASA's needs; thus our data sets are
usually related to Earth and Space Science, including data delivered from instruments
in space, and data produced by telescopes on the ground.
About the Speaker:
Daniel S. Katz is the supervisor of the Parallel Applications Technologies group within
the Modeling and Data Management Systems Section and a principal member of the
Information Systems and Computer Sciences staff at the Jet Propulsion Laboratory (JPL),
California Institute of Technology. He is also Area Program Manager of High End Computing
in the Space Mission Information Technology Office. His research interests include:
numerical methods, algorithms, and programming applied to supercomputing, parallel computing,
cluster computing, and embedded computing; and fault-tolerant computing. He received his B.S.,
M.S., and Ph.D degrees in Electrical Engineering from Northwestern University, Evanston,
Illinois, in 1988, 1990, and 1994, respectively. His work is documented in numerous book
chapters, journal and conference publications, and NASA Tech Briefs. He is a senior member
of the IEEE, and serves on the IEEE Technical Committee on Scalable Computing's Executive
Committee, the IEEE Technical Committee on Parallel Processing's Executive Committee,
and the steering committee for the IEEE Cluster conference series.
