Title: Bridging the Gap between Problems and Solutions in Machine Learning and Data Mining
Abstract:
Much of the art of machine learning and data mining is figuring how to map problems onto available solution methods. Or, stated differently, the set of available tools and techniques color and constrain the problems we are able to solve, the complexity of solving them, and the quality of the eventual solutions. Much of my work over the last two decades has involved developing methods that broaden the tools available for solving problems in machine learning and data mining. In this talk I will present a number of projects I’ve worked on that bridge, or at least narrow, the gap between problems and solutions in such areas as text classification, recommender systems, and description logics. I will also discuss what I see as some of the key challenges looking ahead.
This presentation covers joint work conducted with Chumki Basu, Alex Borgida, William Cohen, Vasant Dhar, Sofus Macskassy, Nina Mishra, Lenny Pitt, Foster Provost, Ramesh Sankaranarayanan, and Sarah Zelikovitz.
Biography:
Haym Hirsh received his BS degree from the Mathematics and Computer Science Departments at UCLA and his MS and PhD from the Computer Science Department at Stanford University. He is a Professor of Computer Science at Rutgers University, and has also held visiting positions at Bar-Ilan University, CMU, MIT, NYU, and the University of Zurich. He is currently Director of the Division of Information and Intelligent Systems at the U.S. National Science Foundation's Directorate for Computer and Information Science and Engineering. Haym's research is on foundations and applications of machine learning, data mining, and information retrieval.
