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Bradley Receives Council on Research and Creative Work Award


April 2002

Elizabeth Bradley photo

Faculty member Elizabeth Bradley has been awarded a Council on Research and Creative Work (CRCW) grant-in-aid for "Feature Recognition in Oceanographic Data". From Bradley's project description:

We are in the process of building a data analysis tool that can find and recognize interesting coherent structures -- "features" -- in large scientific datasets. There are two primary challenges in making such a tool both general and practical; the definition of an interesting feature varies across domains, as do the format and structure of the scientific data involved in identifying one. Meteorologists, for instance, look for hurricanes in wind and pressure data, while astrophysicists find supernovae by comparing telescope photographs, and cell biologists recognize organelles using the density measurements that are implicit in electron micrographs.

Intelligent analysis of any kind of data requires both domain-specific principles -- e.g., what a hurricane looks like, and in what data -- and general mathematics, such as the notion of a threshold or a gradient. Effective automation of the data analysis process relies critically on good algorithms that instantiate both kinds of ideas, and that work well on large amounts of noisy data. For these reasons, the broad field that is variously termed data mining, computer vision, pattern recognition, intelligent data analysis, etc., draws upon ideas and techniques from a very wide variety of disciplines, ranging from statistics, geometry, and topology to digital signal processing and the machine learning branch of artificial intelligence...

Scientific data -- the specific focus of our work -- has received surprisingly little attention in these various research communities. Data mining, for instance, is dominated by applications like detection of credit card fraud in large databases; computer vision largely focuses on segmenting and understanding the pixels in a camera image. The structure and formalisms of science and engineering, however, can provide some serious leverage to feature recognition techniques. Descriptions of the signature of credit card fraud in a specific database are essentially impossible to construct a priori, but scientific reasoning is presumably at least somewhat codifyable. This is useful because data analysis tools work much better if they are provided with explicit mathematical descriptions of the structures that they are to find.

The CRCW was created on October 1, 1935, to encourage and strengthen research and creative work at the University of Colorado. The primary function of CRCW is to provide faculty members with financial assistance and time free from teaching responsibilities so they may pursue their research interests. CRCW awards the Distinguished Research Lectureship, Faculty Fellowships, Junior Faculty Development Awards, grants-in-aid, small grants, and conference support.

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Department of Computer Science
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