Speaker: Daniel B. Neill, Carnegie Mellon University
Location: Warren Weaver Hall 1302
Date: February 19, 2016, 11:30 a.m.
Host: Subhash Khot
Over the past decade, our lab has developed a variety of new statistical and computational approaches for detection of emerging events, and other relevant patterns, in data. This talk will focus on our recent work in scaling up these approaches to deal with the size and complexity of data needed to address important real-world problems at the societal scale. As two concrete examples, we consider detection of emerging outbreaks of disease using Emergency Department visit records, and prediction of civil unrest using online social network data. To deal with the massive size and high dimensionality of real-world data, we propose the fast multidimensional subset scan, a novel approach for accurate and computationally efficient pattern detection. Subset scanning treats the pattern detection problem as a search over subsets of data records and attribute values, finding those subsets which maximize some score function. One key insight is that this search over subsets can be performed exactly and efficiently, reducing run times from years to milliseconds, using the "linear-time subset scanning" property of many commonly used score functions such as likelihood ratio statistics. To deal with the complexity of real-world data, we present a new approach, the non-parametric heterogeneous graph scan, which incorporates heterogeneous social network data into the subset scan framework. Finally, we demonstrate that these approaches achieve more accurate, precise, and computationally efficient detection and prediction of real-world events, as compared to the current state of the art.
This work is in collaboration with many current and former members of Heinz College's Event and Pattern Detection Laboratory (http://epdlab.heinz.cmu.edu) and is supported by funding from the National Science Foundation and MacArthur Foundation.
Daniel B. Neill is the Dean's Career Development Professor and Associate Professor of Information Systems at Carnegie Mellon University's Heinz College, where he directs the Event and Pattern Detection Laboratory. He holds courtesy appointments in the Machine Learning Department and Robotics Institute at CMU's School of Computer Science and is an adjunct professor in the University of Pittsburgh’s Department of Biomedical Informatics. He received his M.Phil. from Cambridge University and his M.S. and Ph.D. in Computer Science from CMU. His research focuses on machine learning and event detection in massive datasets, with applications ranging from medicine and public health to law enforcement and urban analytics. Dr. Neill was the recipient of an NSF CAREER award and an NSF Graduate Research Fellowship, and was named one of the "top ten artificial intelligence researchers to watch" by IEEE Intelligent Systems.
Refreshments will be offered starting 15 minutes prior to the scheduled start of the talk.