Daniel Neill
Graduate Student
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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Anomaly Pattern Detection in Categorical Datasets
(2008)
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A Bayesian scan statistic for spatial cluster detection
(2005)
A new Bayesian method for cluster detection -
A Bayesian spatial scan statistic
(2005)
A new Bayesian method for spatial cluster detection -
Efficient Analytics for Effective Monitoring of Biomedical Security
(2005)
Applying WSARE, SSS, TipMonitor to biomedical security -
Detecting Anomalous Patterns in Pharmacy Retail Data
(2005)
A bio-surveillance system to collect disease outbreak feedback from public health officials - (7 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.