ORIE Colloquium: Daniel Neill (NYU) - Subset Scanning for Event and Pattern Detection

Description

Building on the prior literature on (spatial) scan statistics, subset scanning is an accurate and computationally efficient framework for detecting events and other patterns in both spatial and non-spatial datasets, through constrained optimization of a score function (e.g., a likelihood ratio statistic) over subsets of the data. Many score functions of interest satisfy the linear-time subset scanning property (Neill, 2012), enabling exact and efficient optimization over subsets. This efficient unconstrained optimization step, the fast subset scan, can be used as a building block for scalable solutions to event and pattern detection problems incorporating a variety of real-world constraints. In this talk, I will introduce the fundamental theory and methodology of subset scanning, and various extensions and generalizations of this approach. I will also describe a number of real-world applications of subset scanning, ranging from public health (early detection of disease outbreaks and emerging patterns of drug overdose deaths) to algorithmic fairness (discovering and correcting systematic biases in risk prediction, with applications to criminal justice and many other domains). Bio: Daniel B. Neill is associate professor of computer science and public service at NYU’s Robert F. Wagner Graduate School of Public Service and Courant Institute Department of Computer Science, and associate professor of urban analytics at NYU's Center for Urban Science and Progress. He was previously a tenured faculty member at Carnegie Mellon University’s Heinz College, where he was the Dean’s Career Development Professor, associate professor of information systems, and director of the event and pattern detection laboratory. Daniel's research focuses on developing new methods for machine learning and event detection in massive and complex datasets, with applications ranging from medicine and public health to law enforcement and urban analytics He works closely with organizations including public health, police departments, hospitals, and city leaders to create and deploy data-driven tools and systems to improve the quality of public health, safety, and security, for example, through the early detection of disease outbreaks and through predicting and preventing hot-spots of violent crime. He is also the Associate Editor of four journals (IEEE Intelligent Systems, Decision Sciences, Security Informatics, and ACM Transactions on Management Information Systems). He 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. Daniel received his M.Phil. from Cambridge University and his M.S. and Ph.D. in computer science from Carnegie Mellon University.