Abstract

Abstract: Traditional approaches to sensing and information processing are non-adaptive in the sense that all data are collected prior to analysis and processing. One can envision, however, adaptive strategies in which information gleaned from previously collected data is used to guide the selection of new data. This talk focuses on the emerging theory and practice of such `active learning` methods. I will show that automatic feedback from data analysis to data collection can be crucial for effective learning and inference. To illustrate the role of feedback, I will discuss two inference problems. First, I will consider binary-valued prediction (classification) problems. Under a fixed sample budget, the prediction errors of active learning methods can be exponentially smaller than those of non-adaptive methods. Second, motivated by problems in systems biology, I will discuss the role of active learning in the recovery of sparse signals in noise. I will show that certain weak, sparse patterns are imperceptible in non-adaptive measurements, but can be recovered perfectly using adaptive sequential experimental designs.

Biography: Robert Nowak received the B.S. (with highest distinction), M.S., and Ph.D. degrees in electrical engineering from the University of Wisconsin-Madison in 1990, 1992, and 1995, respectively. He was a Postdoctoral Fellow at Rice University in 1995-1996, an Assistant Professor at Michigan State University from 1996-1999, held Assistant and Associate Professor positions at Rice University from 1999-2003, and was a Visiting Professor at INRIA in 2001. Nowak is now the McFarland-Bascom Professor of Engineering at the University of Wisconsin-Madison and a Visiting Fellow at Trinity College, Cambridge. He has served as an Associate Editor for the IEEE Transactions on Image Processing, the Secretary of the SIAM Activity Group on Imaging Science, and is currently an Associate Editor for the ACM Transactions on Sensor Networks. He was General Chair for the 2007 IEEE Statistical Signal Processing workshop and Technical Program Chair for the 2003 IEEE Statistical Signal Processing Workshop and the 2004 IEEE /ACM International Symposium on Information Processing in Sensor Networks. Dr. Nowak received the General Electric Genius of Invention Award in 1993, the National Science Foundation CAREER Award in 1997, the Army Research Office Young Investigator Program Award in 1999, the Office of Naval Research Young Investigator Program Award in 2000, and IEEE Signal Processing Society Young Author Best Paper Award in 2000. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). His research interests include signal processing, machine learning, imaging and network science, and applications in communications and systems biology.