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Daniel B. Neill
Assistant Professor of Information Systems
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Email: dbn@andrew.cmu.edu
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Biography
Daniel B. Neill is an Assistant Professor of Information Systems at the Heinz School. He also holds a courtesy appointment in the Machine Learning Department, School of Computer Science. He is a member of the Auton Laboratory (School of Computer Science), the RODS Laboratory (University of Pittsburgh), Sigma Xi, and the International Society for Disease Surveillance. He earned his M.Phil. in Computer Speech at Cambridge University (2002), his M.S. in Computer Science at Carnegie Mellon (2004), and his Ph.D. in Computer Science at Carnegie Mellon (2006).
Prof. Neill was a recipient of the Winston Churchill Scholarship and NSF Graduate Research Fellowship. He received the best paper award at the National Syndromic Surveillance Conference in 2005 for his work on Bayesian spatial scan statistics.
Prof. Neill’s research interests include machine learning, data mining, artificial intelligence, game theory, and natural language processing. He is particularly interested in developing methods for automatic detection and investigation of “interesting” patterns in massive real-world datasets. A major focus of his recent work has been cluster detection, with the goal of developing an automatic system for nationwide disease surveillance. The current implementation of this system monitors daily public health data from over 20,000 hospitals and pharmacies nationwide, automatically detects emerging outbreaks of disease, and reports alerts to state and local public health departments. His work has appeared in a variety of journals and collections, including Advances in Neural Information Processing Systems, Advances in Disease Surveillance, Journal of Theoretical Biology, and Rationality and Society.
Prof. Neill teaches the core statistics course for the MISM program (95-796, Statistics for IT Managers). He is in the process of developing a course on “Artificial Intelligence Tools for Policy,” which will be taught starting in the 2007-08 academic year.
Selected Publications
Cluster Detection and Disease Surveillance:
Daniel B. Neill and Andrew W. Moore. Methods for detecting spatial and spatio-temporal clusters. In M. Wagner, A. Moore, and R. Aryel, eds., Handbook of Biosurveillance, 2006.
Daniel B. Neill, Andrew W. Moore, and Gregory F. Cooper. A Bayesian spatial scan statistic. In Advances in Neural Information Processing Systems 18, 1003-1010, 2006.
(pdf)
Daniel B. Neill, Andrew W. Moore, Maheshkumar Sabhnani, and Kenny Daniel. Detection of emerging space-time clusters. Proceedings of the 11th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 218-227, 2005. (pdf)
Daniel B. Neill, Andrew W. Moore, Francisco Pereira, and Tom Mitchell. Detecting significant multidimensional spatial clusters. In Advances in Neural Information Processing Systems 17, 969-976, 2005. (pdf)
Michael M. Wagner, F.-C. Tsui, J. Espino, W. Hogan, J. Hutman, J. Hersh, D. Neill, A. Moore, G. Parks, C. Lewis, and R. Aller. A national retail data monitor for public health surveillance. Morbidity and Mortality Weekly Report, Supplement on Syndromic Surveillance, 53: 40-42, 2004.
Daniel B. Neill and Andrew W. Moore. Rapid detection of significant spatial clusters. Proceedings of the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 256-265, 2004. (pdf)
Game Theory:
Daniel B. Neill. Cascade effects in heterogeneous populations. Rationality and Society 17(2): 191-241, 2005. (pdf)
Daniel B. Neill. Evolutionary stability for large populations. Journal of Theoretical Biology 227(3): 397-401, 2004. (pdf)
Daniel B. Neill. Optimality under noise: higher memory strategies for the Alternating Prisoner's Dilemma. Journal of Theoretical Biology 211(2): 159-180, 2001. (pdf)
Natural Language Processing:
Paul Hsiung, Andrew Moore, Daniel Neill, and Jeff Schneider. Alias detection in link data sets. Proceedings of the First International Conference on Intelligence Analysis, 2005. (pdf)
Daniel B. Neill. Fully automatic word sense induction by semantic clustering. Cambridge University, M.Phil. thesis, 2002. (pdf)
Education
PhD, Computer Science, Carnegie Mellon University
Representative Publications
Working Papers
An Information Visualization Approach to Classification and Assessment of Diabetes Risk in Primary Care
An Information Visualization Approach to Classification and Assessment of Diabetes Risk in Primary Care
