[Research] thesis proposal

[Daniel B. Neill]

Dear friends,

You are all cordially invited to my thesis defense next Monday, June 5, at
10:30am in Wean 5409.  I've attached an abstract below; please feel free
to forward this to anyone else who may be interested.

All the best,
Daniel

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Detection of Spatial and Spatio-Temporal Clusters
Daniel B. Neill
Monday June 5, 10:30am, Wean 5409

Thesis committee:

Andrew Moore (chair), CMU/Google
Tom Mitchell, CMU
Jeff Schneider, CMU
Gregory Cooper, University of Pittsburgh
Andrew Lawson, University of South Carolina

Abstract:

This thesis develops a general and powerful statistical framework for the
automatic detection of spatial and space-time clusters.  Our "generalized
spatial scan" framework is a flexible, model-based framework for accurate
and computationally efficient cluster detection in diverse application
domains.  Through the development of the "fast spatial scan" algorithm and
new Bayesian cluster detection methods, we can detect clusters hundreds or
thousands of times faster than previous approaches.  More timely detection
of emerging clusters (with high detection power and low false positive
rates) was made possible by the development of "expectation-based" scan
statistics, which learn baseline models from past data then detect regions
that are anomalous given these expectations.  These cluster detection
methods were applied to two real-world problem domains: the early
detection of emerging disease epidemics, and the detection of clusters of
brain activity using fMRI brain imaging data.  One major contribution of
this work is the development of the SSS system for nationwide disease
surveillance, currently used in daily practice by several state and local
health departments.  This system receives data (including emergency
department records and medication sales) from over 20,000 stores and
hospitals nationwide, automatically detects emerging clusters of disease,
and reports these results to public health officials.  Through
retrospective case studies and semi-synthetic testing, we have shown that
our system can detect outbreaks significantly faster than previous disease
surveillance methods.