H. T. Kung
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|H. T. Kung|
|Born||November 9, 1945|
|Institutions||Carnegie Mellon University
|Alma mater||National Tsing Hua University
Carnegie Mellon University
|Thesis||Topics in Analytic Computational Complexity (1974)|
|Doctoral advisor||Joseph F. Traub|
|Doctoral students||Brad Karp
Monica S. Lam
Charles E. Leiserson
Robert T. Morris
|Notable awards||Member of National Academy of Engineering
Academician of Academia Sinica
IEEE Computer Society Charles Babbage Award
H. T. Kung (Kung, Hsiang-Tsung; Chinese: 孔祥重; pinyin: Kong Xiangchong; b. November 9, 1945) is a Chinese American computer scientist. His research is primarily in the area of machine learning, signal processing and compressive sensing, and parallel computing, but his interests have been broad-ranging, including computational complexity theory, database theory, VLSI design, and parallel computing.
Kung received his bachelor's degree in mathematics from National Tsing Hua University in 1968 and Ph.D. from Carnegie Mellon University in 1974, and first taught there, where his research included work on novel parallel computers and the popularization of the Systolic array. He joined Harvard University in 1992, where he is the William H. Gates Professor of Computer Science and Electrical Engineering. Kung also is co-chair of Harvard's "PhD in Information, Technology and Management" Program.
Kung is a member of the Academia Sinica in Taiwan, and also of the National Academy of Engineering in the United States. He is a recipient of the Inventor of the Year Award by the Pittsburgh Intellectual Property Law Association in 1991.
Kung's contributions include: Systolic Arrays; iWarp; Optimistic concurrency control, a core principle underlying many transactional memory and database implementations, including Google App Engine, and Ruby on Rails's data management protocol; Read-Copy-Update, a mutual exclusion synchronization method, deployed in the modern Linux kernel; Greedy Perimeter Stateless Routing, a location-based routing protocol for mesh networks; a communication-avoiding optimal distributed matrix multiplication algorithm; the Kung-Traub algorithm for comparing the expansion of an algebraic function; etc.
- "Alumni H.T Kung" (in Chinese).
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