Shang-Hua Teng

Shang-Hua Teng is the Seeley G. Mudd Professor of Computer Science and Mathematics at the University of Southern California, holding a joint appointment in the Viterbi School of Engineering and Department of Computer Science. Renowned as one of the most original theoretical computer scientists globally, his work fundamentally reshapes algorithmic frameworks across multiple disciplines.

Professor Teng earned his B.S. from Shanghai Jiao Tong University and Ph.D. in Computer Science from Carnegie Mellon University. His academic journey includes professorships at Boston University and Carnegie Mellon University prior to joining USC.

His research revolutionized theoretical computer science through groundbreaking contributions in algorithm design, particularly smoothed analysis (developed with Dan Spielman), which bridges worst-case and average-case complexity to explain real-world algorithm performance. This framework underpins advances in optimization, machine learning, and data mining, while his spectral graph theory work provides foundational tools for network analysis and computational economics.

Key accolades include two Gödel Prizes—2008 for smoothed analysis and 2015 for nearly linear-time Laplacian solvers—and recognition as a Simons Investigator, reflecting sustained excellence in theoretical research.

As a Simons Investigator, Teng directs substantial research funding toward developing scalable algorithms for massive datasets. He actively mentors doctoral students and postdoctoral researchers, advancing next-generation computational theory while leading collaborative projects at the intersection of computer science and applied mathematics.