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Shanghua Teng is the Seeley G. Mudd Professor of Computer Science and an Engineering University Professor. He earned his Doctoral Degree in Computer Science from Carnegie Mellon University, a Master's Degree in Computer Science from the University of Southern California, and two Bachelor's Degrees in Electrical Engineering and Computer Science from Shanghai Jiao-Tong University. Teng has received significant recognition in theoretical computer science, winning the prestigious Gödel Prize twice for his groundbreaking work on smoothed analysis and nearly-linear time Laplacian solvers. He is known for his collaborative research with Dan Spielman at Yale and others to tackle fundamental algorithms and optimization problems. His work has led to developments in network analysis and computational economics, including characterizing complexities in game theory and approximation of Nash equilibria. Teng has also contributed to advancements in numerical simulations used in significant technological applications, received numerous awards, including the ACM Fellow designation and Fulkerson Prize, and has been recognized by the Simons Foundation as a Simons Investigator in 2014. His extensive work includes patent contributions and consulting roles at major technology companies.
University of Southern California • Los Angeles, CA
Professor in Computer Science with extensive research and teaching experience.
GRE is NOT required for Master's applicants for 2025-2026.