[Research] thesis defense

[Daniel B. Neill]

Dear friends,

I passed-- let's celebrate!  Rebecca and I hope that you will be able to
join us tonight, 8pm at Harris Grill in Shadyside.  Please feel free to
bring friends and significant others-- just RSVP to me by 6pm so I can
provide the restaurant with a final count.

All the best,
Daniel

P.S. Even if you missed the defense, join us tonight anyway!


On Mon, 29 May 2006, Daniel B. Neill wrote:

> 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
>
> -------------------------------------------------------------------------
> 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.
>
>
>