Rate My Professor Shang-Hua Teng

Shang-Hua Teng is the Seeley G. Mudd Professor of Engineering and University Professor of Computer Science and Mathematics in the Department of Computer Science at the University of Southern California Viterbi School of Engineering. He previously served as Chair of the Computer Science Department from 2009 to 2012. Teng earned a B.S. in Computer Science and a B.A. in Electrical Engineering from Shanghai Jiao Tong University in 1985, an M.S. in Computer Science from the University of Southern California in 1988, and a Ph.D. in Computer Science from Carnegie Mellon University in 1991. His academic career includes faculty positions at Boston University from 2002 to 2009, prior appointments at the University of Illinois at Urbana-Champaign, and research roles at institutions such as Xerox PARC, NASA Ames Research Center, IBM Almaden Research Center, Akamai Technologies, and Microsoft Research.

Teng's research specializations encompass smoothed analysis of algorithms, computational economics and game theory, spectral graph theory, scientific computing, mathematical programming, combinatorial optimization, computational geometry, and computer graphics. He pioneered well-shaped Delaunay meshing algorithms for three-dimensional geometric domains and has consulted for leading technology companies, securing fifteen patents. Major awards include the Gödel Prize in 2008 for developing the theory of smoothed analysis with Daniel Spielman and in 2015 for nearly-linear time Laplacian solvers, the 2009 Fulkerson Prize from the American Mathematical Society and Mathematical Programming Society, the 2010 ACM Fellowship, the 2011 ACM STOC Best Paper Award, and the 2014 Simons Investigator award. Key publications feature 'Smoothed Analysis of Algorithms: Why the Simplex Algorithm Usually Takes Polynomial Time' with Daniel A. Spielman, work on spectral sparsification and graph partitioning, and 'Scalable Algorithms for Data and Network Analysis' in 2016. Teng's contributions have significantly advanced theoretical computer science, enhancing algorithm design and practical applications across disciplines.