Li-Yang Tan

Li-Yang Tan is an Assistant Professor of Computer Science at Stanford University. His research focuses on theoretical computer science, particularly complexity theory and its intersections with machine learning and algorithm design. He holds a Ph.D. from Columbia University, advised by Rocco Servedio, and has been recognized with prestigious awards including the Sloan Fellowship and best paper awards at FOCS, CCC, and SAT conferences.

His work bridges foundational theoretical contributions with practical algorithmic innovations, emphasizing topics like decision tree learning, computational hardness, and pseudorandomness. Notable achievements include polynomial-time decision tree learning algorithms, hardness results for learning problems, and contributions to coding theory and property testing. He collaborates extensively with researchers such as Guy Blanc, Caleb Koch, and Carmen Strassle on projects addressing core challenges in computational complexity.

Tan's publications span top venues like FOCS, STOC, and JACM, reflecting his impact on areas such as query complexity, certification algorithms, and parallel computation lower bounds. His research also extends to cryptographic primitives and information-theoretic methods, showcasing a broad yet deeply technical approach to theoretical computer science.