Daniel Daniel B. Neill
Assistant Professor of Information Systems
Heinz School of Public Policy and Management
Carnegie Mellon University
Hamburg Hall #2105B, x8-3885.

neill@cs.cmu.edu

I am an Assistant Professor of Information Systems in the Heinz School of Public Policy at Carnegie Mellon University. I also hold courtesy appointments in the Machine Learning Department and Robotics Institute at CMU, and an adjunct appointment in the Department of Biomedical Informatics at the University of Pittsburgh. I am a member of the Auton Laboratory and RODS Laboratory.

I recently finished my Ph.D. in the Department of Computer Science at CMU, advised by Andrew Moore. Before that, I received my B.S.E. in Electrical Engineering and Computer Science from Duke University in 2001, my M.Phil. in Computer Speech from Cambridge University in 2002, and my M.S. in Computer Science from Carnegie Mellon in 2004.

Teaching:

95-796, Statistics for IT Managers (course page for Fall 2006) (on Blackboard starting Spring 2007)
90-866, Artificial Intelligence Tools for Policy (course description). **NEW COURSE, STARTING SPRING 2008**

Research:

Slides for my SCS immigration course talk are now available here.

I am currently researching fast algorithms and new statistical methods for the detection of spatial and spatio-temporal clusters. One major application of this work is the rapid and automatic detection of emerging disease outbreaks. Some of my other research interests include statistical machine learning, data mining, algorithms, and game theory. A more detailed description of my research is available here.

I am currently seeking students in Heinz and SCS for several research projects:

1. Machine Learning for Disease Surveillance

Automatic disease surveillance systems are essential for early detection of public health threats such as bird flu or bioterrorism. We have developed a system which monitors nationwide public health data (including hospital visits and pharmacy sales) and automatically detects emerging outbreaks of disease. The current system uses new statistical machine learning techniques and fast, scalable algorithms to rapidly detect anomalous disease clusters in massive real-world datasets.

We plan to extend this system in a variety of ways, including: 2. Anomalous Pattern Detection

We plan to investigate a variety of large-scale anomaly detection problems, including network intrusion detection, terrorist group detection, environmental monitoring of water quality, and tumor detection in medical images. Rather than searching for individual data points that are anomalous, interesting, or unexpected, these problems require us to detect groups of data points with interesting patterns or relationships. Building on our prior work in spatial cluster detection, we are working to develop general and powerful statistical methods, and fast algorithms, for anomalous pattern detection in massive, high-dimensional datasets.

Here are links to some recent papers and presentations; a complete list of publications is available in my CV.

CLUSTER DETECTION AND DISEASE SURVEILLANCE

Daniel B. Neill. Detection of spatial and spatio-temporal clusters. Ph.D. thesis, Carnegie Mellon University, Department of Computer Science, Technical Report CMU-CS-06-142, 2006. (pdf)

Daniel B. Neill, Andrew W. Moore, and Gregory F. Cooper. A Bayesian spatial scan statistic. In Y. Weiss, et al., eds. Advances in Neural Information Processing Systems 18, 1003-1010, 2006. (abstract) (pdf) (ppt presentation)

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. (abstract) (pdf) (ppt presentation)

Daniel B. Neill and Andrew W. Moore. Anomalous spatial cluster detection. Proceedings of the KDD 2005 Workshop on Data Mining Methods for Anomaly Detection, 2005. (pdf) (ppt presentation)

Maheshkumar R. Sabhnani, Daniel B. Neill, Andrew W. Moore, Fu-Chiang Tsui, Michael M. Wagner, and Jeremy U. Espino. Detecting anomalous patterns in pharmacy retail data. Proceedings of the KDD 2005 Workshop on Data Mining Methods for Anomaly Detection, 2005. (pdf)

Daniel B. Neill, Andrew W. Moore, Francisco Pereira, and Tom Mitchell. Detecting significant multidimensional spatial clusters. In L.K. Saul, et al., eds. Advances in Neural Information Processing Systems 17, 969-976, 2005. (abstract) (pdf)

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. (abstract) (pdf) (ppt presentation)

Daniel B. Neill and Andrew W. Moore. A fast multi-resolution method for detection of significant spatial disease clusters. In S. Thrun et al., eds. Advances in Neural Information Processing Systems 16, 651-658, 2004. (abstract) (pdf)

GAME THEORY

Daniel B. Neill. Cascade effects in heterogeneous populations. Rationality and Society 17(2): 191-241, 2005. (abstract) (pdf)

Daniel B. Neill. A tutorial on game theory. Carnegie Mellon University, 2004. (ppt presentation)

Daniel B. Neill. Evolutionary stability for large populations. Journal of Theoretical Biology 227(3): 397-401, 2004. (abstract) (pdf)

Daniel B. Neill. Evolutionary dynamics with large aggregate shocks. Dept. of Computer Science, Technical Report CMU-CS-03-197, 2003. (pdf)

Daniel B. Neill. Cooperation and coordination in the Turn-Taking Dilemma. Proceedings of the Ninth Conference on Theoretical Aspects of Rationality and Knowledge: 231-244, 2003. (abstract) (ps) (ppt presentation)

Daniel B. Neill. Optimality under noise: higher memory strategies for the Alternating Prisoner's Dilemma. Journal of Theoretical Biology 211(2): 159-180, 2001. (abstract) (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, masters thesis, M.Phil. in Computer Speech, 2002. (abstract) (pdf) (short version) (ppt presentation)

Links:

My Poetry
My Pictures

Google
CNN.com
The Onion
Arts and Letters Daily