Yoav Freund

Professor, Computer Science & Engineering

Computational learning theory and related areas in probability theory, information theory, statistics and pattern recognition.

Yoav Freund works on applications of machine learning algorithms in bioinformatics, computer vision, finance, network routing and high-performance computing. He has developed a new approach to the study and development of machine learning algorithms, where the goal is to produce a good decision algorithm for a repetitive decision task. A decision algorithm receives as input an instance (sensory data) and outputs a decision (an action). After the decision has been made, there is a measurable outcome. Freund's main focus is on binary classification tasks, where the decision is binary, the outcome is binary, and the loss is 1 if the decision and outcome don't match and 0 if they do. Given these definitions and a source of instances and outcomes, Freund can evaluate the performance of any decision algorithm--treating it as a "black box". The practical advantage of the black-box approach is that it provides a measuring stick for comparing all types of decision algorithms, regardless of how they are constructed or analyzed. By extension, this approach produces a way of comparing all types of learning algorithms.

Capsule Bio:
Yoav Freund is a professor of Computer Science and Engineering at UC San Diego. His work is in the area of machine learning, computational statistics and their applications. Dr. Freund is an internationally known researcher in the field of machine learning, a field which bridges computer science and statistics. He is best known for his joint work with Dr. Robert Schapire on the Adaboost algorithm. For this work they were awarded the 2003 Gödel prize in Theoretical Computer Science, as well as the Kanellakis Prize in 2004.

Institute Affiliation:
California Institute for Telecommunications and Information Technology »