Our People

Daniel B. Neill

Visiting Professor of Urban Analytics dbn2@nyu.edu (646) 997-0588
Biography
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Daniel B. Neill comes to NYU CUSP as a Visiting Professor of Urban Analytics.  He is currently on leave from his tenured faculty position at Carnegie Mellon University’s Heinz College, where he has been the Dean’s Career Development Professor, Associate Professor of Information Systems, and Director of the Event and Pattern Detection Laboratory. He holds courtesy appointments in the Machine Learning Department and Robotics Institute at Carnegie Mellon’s School of Computer Science and is an adjunct professor at 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 Carnegie Mellon University.  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. Prof. 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.

For more information, please visit Prof. Neill’s personal web page, http://www.cs.cmu.edu/~neill, and the Event and Pattern Detection Laboratory web page, http://epdlab.heinz.cmu.edu.

Publications
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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.