Li-Yang Tan

Li-Yang Tan is an Assistant Professor of Computer Science at Stanford University, focusing on theoretical computer science. His research emphasizes computational complexity, machine learning theory, and algorithm design.

• Education: Ph.D. in Computer Science from Columbia University, advised by Rocco Servedio

His work explores:

• Boolean function complexity
• Decision tree learning algorithms
• Circuit lower bounds
• Computational-statistical tradeoffs
• Query complexity
• Massively parallel algorithms

Recent publications analyze computational-statistical tradeoffs via NP-hardness, improve decision tree learning techniques, and establish direct sum theorems for query complexity. His research often bridges complexity theory, learning theory, and algorithm design, with applications in pseudorandomness and correlation clustering.

• Awards:
  • Best Paper Award at FOCS
  • Best Paper Award at CCC
  • Best Paper Award at SAT
  • Sloan Fellowship

Li-Yang collaborates with students and researchers including Guy Blanc, Caleb Koch, and Carmen Strassle. He has delivered invited special issue papers at FOCS, CCC, and SAT conferences.