CS Seminar: Dr. Jason Corso

Event Date: 
Mon, 2014-03-24 10:00 - 11:30

Dr. Jason Corso

SUNY Buffalo

Monday, March 24, 2014 10:10AM-11:30AM 655 McBryde

Why Label When You Can Compare? Active Constraint Pursuit in Metric Learning and Clustering

Relating pairs of samples, by way of similarity functions or distance metrics, is at the heart of machine learning. In some applications, such as face-photo organization in which we do not know the identities in the photos, specifying pairwise links is the only way to handle the learning problem—and even naive users can provide the annotations. In this talk, I will cover my recent work in metric learning and active pairwise constraint pursuit. For metric learning, I will present our efficient max-margin metric learning that learns a Mahalanobis metric, as well as our random forest distance method that specifies a space-varying non- linear distance function. In active constraint pursuit for semi-supervised clustering, I will discuss how we use the gradient of the spectral decomposition to select the next best constraints for active user queries. Time-permitting, I will present applications of these methods to real data.

Dr. Jason Corso is an associate professor of Computer Science and Engineering at SUNY Buffalo. He received his Ph.D. at The Johns Hopkins University in 2006 (from the Computational Interaction with Physical Systems Lab), the M.S.E Degree from The Johns Hopkins University in 2002 and the B.S. Degree with honors from Loyola College In Maryland. He spent two years as a post-doctoral fellow at the University of California, Los Angeles affiliated with Medical Imaging Informatics, the Laboratory of Neuro Imaging, and the Center for Image and Vision Science. He is the recipient of the Army Research Office Young Investigator Award 2010 (robotics), NSF CAREER award 2009 (computer vision), SUNY Buffalo Young Investigator Award 2011, a member of the 2009 DARPA Computer Science Study Group (data mining), and a recipient of the Link Foundation Fellowship in Advanced Simulation and Training (physically-grounded vision). Corso has authored more than ninety peer-reviewed papers on topics of his research interest including machine learning, computer vision, robotics, data science, and medical imaging. He is a member of the AAAI, IEEE and the ACM. He is PI on more than $5 million in research funding from major federal agencies, including NSF, NIH, DARPA, ARO, and IARPA.