Daniel Neill
Assistant Professor of Information Systems, H. John Heinz III School of Public Policy and Management
Research Interests
My main research interests are statistical machine learning and game theory. I am currently working on fast methods for detecting spatial overdensities (for example, clusters of disease cases).
Tags
Biosurveillance, Clustering, Efficient Statistical Algorithms, Kd-trees and Ball-trees, Link Analysis, Spatial Statistics
Recent Papers
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T-Cube Web Interface in Support of Real-Time Bio-surveillance Program
(2009)
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Anomaly Pattern Detection in Categorical Datasets
(2008)
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Efficient Analytics for Effective Monitoring of Biomedical Security
(2005)
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Alias Detection in Link Data Sets
(2005)
Combining string similarity with contextual similarity when searching for aliases using active learning. -
Detecting Significant Multidimensional Spatial Clusters
(2005)
Applying the fast multidimensional spatial scan statistic to detect clusters in epidemiological and brain imaging data. - (8 more)
Software
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Scan Statistics
A fast implementation of scan statistic search for spatial overdensities. Our goal is to find rectangular regions where the count (e.g. number of disease cases) is higher than expected, given the underlying population distribution.