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Shang-Hua Teng has been recognized for his significant contributions to theoretical computer science, having won the prestigious Gödel Prize in 2008 and again in 2015. His research focuses on smoothed analysis, a fundamental approach in understanding the performance of algorithms, and the development of nearly-linear time Laplacian solvers for network systems. Teng has collaborated extensively with notable figures in the field, such as Dan Spielman from Yale University, resulting in advancements in optimization and network computation problems. He was named a Simons Investigator by the Simons Foundation in 2014 due to his curiosity-driven research pursuits. His work has earned him various awards, including the ACM STOC Paper Award and election as an ACM fellow. Teng has also contributed to the field of computational economics and game theory, which includes characterizing the complexity of computing approximate Nash equilibria. His interests extend beyond theoretical work; he has also been involved in game design, collaborating with his Ph.D. student to analyze mathematical board games. Over his career, Teng has worked with renowned organizations such as Microsoft Research, IBM Almaden Research Center, and NASA Ames Research Center, and holds fifteen patents related to compiler optimization and social networks.
GRE is NOT required for Master's applicants for 2025-2026.