Steve Hanneke
Assistant Professor of Computer Science
Steve's research explores the theory of machine learning, with a focus on reducing the number of training examples sufficient for learning. His work develops new approaches to supervised, semi-supervised, active, and transfer learning, and also revisits the basic probabilistic assumptions at the foundation of learning theory.
Joined department: Fall 2021
Research Areas
- Artificial Intelligence, Machine Learning, and Natural Language Processing
- Theory of Computing, Algorithms, and Quantum Computing
Education
B.S., University of Illinois at Urbana-Champaign, Computer Science (2005)
Ph.D., Carnegie Mellon University, Machine Learning (2009)
Steve's research explores the theory of machine learning, with a focus on reducing the number of training examples sufficient for learning. His work develops new approaches to supervised, semi-supervised, active, and transfer learning, and also revisits the basic probabilistic assumptions at the foundation of learning theory.