Speaker: Christian R. Shelton
Affiliation: UC Riverside
Abstract: First, we model ICU data using a marked point process model, the piecewise-constant conditional intensity model (PCIM). We apply PCIMs and co-clustering to find data-driven patient clusters from 10,000 patient episodes over 10 years at Children’s Hospital Los Angeles. Our clusters are better enriched with respect to mortality than other clustering methods and methods using discrete-time models.
Second, we develop a posterior sampler for PCIMs, built on Poisson processing thinning. The reversible-jump auxiliary Gibbs sampler allows general posterior distribution inference over a wide class of missing data patterns. In video activity segmentation and recognition, it provides state-of-the-art results.
Biography: Dr. Shelton is a Professor of Computer Science at the University of California at Riverside. He has been on the faculty since 2003. His research interests are in statistical approaches in artificial intelligence, with a focus on machine learning and dynamic systems. He has applied his work to areas ranging from computer vision to sociology to medical informatics.
Dr. Shelton received his B.S. in Computer Science from Stanford University in 1996. He then obtained his Ph.D. from MIT in 2001 and returned to Stanford from 2001 to 2003 as a post-doctoral scholar. He spent six months in 2003 and 2004 as a visiting faculty member at Intel Research and the 2012-13 academic year as a visiting researcher at Children’s Hospital Los Angeles. He has been the Managing Editor of the Journal of Machine Learning Research and on the editorial board of the Editorial Board of the Journal of Artificial Intelligence Research.
Date(s) - Aug 02, 2016
11:00 am - 12:00 pm
E-IV Tesla Room #53-125
420 Westwood Plaza - 5th Flr., Los Angeles CA 90095