Speaker: Shang-Hua Teng University of Southern California
Time: 2024-12-24 14:00-2024-12-24 15:00
Venue: Lecture Hall, FIT Building

Abstract:

"Thinking outside the box" has long been a defining trait of theoretical computer science. As a field, we value elegant theories, enlightening proofs, and insightful — sometimes unexpected — connections. However, we also look beyond theory to the practical world, seeking inspiration, establishing links, and explaining empirical trends. We aim for models that capture the essence of fundamental tasks, and for theories that shed insight on basic phenomena in computing. In this talk, I will highlight how a long journey towards understanding a few fundamental, yet fuzzy, concepts in computing—specifically, “heuristics” (in algorithm design and AI), “regularization” (in machine learning), and “strategies” (in game and combinatorial game theory)—has led to the development of new conceptual frameworks, algorithmic techniques, and mathematical theories.

Short Bio:

Shang-Hua Teng is a University Professor and Seely G. Mudd Professor of Computer Science and Mathematics at USC. He is a fellow of SIAM, ACM, and Alfred P. Sloan Foundation, and has twice won the Gödel Prize, first in 2008, for developing smoothed analysis, and then in 2015, for designing the breakthrough scalable Laplacian solver. Citing him as, “one of the most original theoretical computer scientists in the world”, the Simons Foundation named him a 2014 Simons Investigator to pursue long-term curiosity-driven fundamental research. He also received the 2009 Fulkerson Prize,  2023 Science & Technology Award for Overseas Chinese from the China Computer Federation, 2022 ACM SIGecom Test of Time Award (for settling the complexity of computing a Nash equilibrium), 2021 ACM STOC Test of Time Award (for smoothed analysis), 2020 Phi Kappa Phi Faculty Recognition Award (2020)  for his book Scalable Algorithms for Data and Network Analysis, 2011 ACM STOC Best Paper Award (for improving maximum-flow minimum-cut algorithms). In addition, he and collaborators developed the first optimal well-shaped Delaunay mesh generation algorithms for arbitrary three-dimensional domains, settled the Rousseeuw-Hubert regression-depth conjecture in robust statistics, and resolved two long-standing complexity-theoretical questions regarding the Sprague-Grundy theorem in combinatorial game theory. For his industry work with Xerox, NASA, Intel, IBM, Akamai, and Microsoft, he received fifteen patents in areas including compiler optimization, Internet technology, and social networks. Dedicated to teaching his daughter to speak Chinese as the sole Chinese-speaking parent in an otherwise English-speaking family and environment, he has also become fascinated with children's bilingual learning.