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Directory
Faculty
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Craig ChaseThe Raytheon Company Faculty FellowshipAssociate Professor |
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Website
http://www.ece.utexas.edu/~chase/
Research Areas
Computer Architecture & Embedded Processors
Computer Engineering
Software Engineering
Biography
Dr. Craig Chase is an Associate Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin and holds the Raytheon Company Faculty Fellowship.
He received his B.S. degree in electrical engineering from Cornell University in 1986, the M.S. in electrical engineering from Purdue University in 1987 and a Ph.D. in electrical engineering from Cornell University in 1993. From 1986 through 1989, Dr. Chase was employed by Bellcore where he worked on next-generation telecommunications service concepts.
Since 1993, he has been on the faculty of the Department of Electrical and Computer Engineering at The University of Texas at Austin. In 1993, Professor Chase was awarded the Jack Kilby/Texas Instruments Endowed Faculty Fellowship in Engineering, and in 2000, Professor Chase received the Hughes Aircraft Endowed Faculty Fellowship in Engineering. In 1999, Professor Chase was recognized by the College of Engineering with the award for outstanding teaching by an assistant professor. Professor Chase is a dedicated instructor and teaches courses in software development and computer architecture at both the graduate and undergraduate levels.
Research Interests:
Professor Chase's research is in the area of high-performance computing. He has supervised three Ph.D. dissertations and more than 20 M.S. reports and thesis. The current focus of his research is on-the-fly parallelization of programs. His objective is to develop tools, programming techniques, and computer system designs that permit programs to be automatically distributed across multiple computers without any change to the source code. The general approach he uses is to recompile the programs as they are running, using the knowledge of the actual behavior of the program to make better optimization decisions. This approach is particularly attractive for programming languages like Java, C# and Perl that are either interpreted or compiled on-the-fly normally.
