Headshot of Daniel B. Neill

Associate Professor of Urban Analytics

Associate Professor of Computer Science and Public Service

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.  Please see Dr. Neill’s personal webpage (http://www.cs.nyu.edu/~neill) for more information.

Daniel received his M.Phil. from Cambridge University and his M.S. and Ph.D. in Computer Science from Carnegie Mellon University.

D. B. Neill. Subset scanning for event and pattern detection. In S. Shekhar and H. Xiong, eds., Encyclopedia of GIS, 2nd ed., 2015, in press.

S. Speakman, S. Somanchi, E. McFowland III, and D. B. Neill. Disease surveillance, case study. In R. Alhajj and J. Rokne, eds., Encyclopedia of Social Network Analysis and Mining, 380-385, 2014.

D. B. Neill, G. F. Cooper, K. Das, X. Jiang, and J. Schneider. Bayesian network scan statistics for multivariate pattern detection. In J. Glaz, V. Pozdnyakov, and S. Wallenstein, eds., Scan Statistics: Methods and Applications, 221-250, 2009.

D. B. Neill and A. W. Moore. Methods for detecting spatial and spatio-temporal clusters. In M. Wagner, A. Moore, and R. Aryel, eds., Handbook of Biosurveillance, 243-254, 2006.

D. B. Neill and A. W. Moore. Efficient scan statistic computations. In A. Lawson and K. Kleinman, eds., Spatial and Syndromic Surveillance for Public Health. Chichester, UK: Wiley, 189-202, 2005.