Liu Yang

Liu Yang is a postdoctoral fellow in the Computer Science Department at Carnegie Mellon University, with a PhD from CMU under Avrim Blum and Jaime Carbonell. His research focuses on Theoretical Machine Learning and Theoretical Computer Science, exploring areas like Statistical Learning Theory, Property Testing, and Algorithmic Economics. He has contributed to active learning, transfer learning, and online pricing problems through mathematical frameworks.

Research Interests: Liu's work bridges Computational Learning Theory with Algorithmic Economics, including:

• Mathematical theories for active property testing of Boolean functions
• Transfer learning with applications to online allocation and pricing
• Analysis of convex losses and statistical identifiability in learning
• Developing Buys-in-Bulk models for active learning efficiency

Service and Teaching: He has served on program committees for ICML 2012-2013, reviewed for top-tier venues, and taught courses like Graduate Algorithms and Modern Computer Algebra at CMU. He also co-developed the DistLearnKit MATLAB toolkit for distance metric learning.