The mission of the CDI is to conduct high quality research in the application of computational techniques to the management and understanding of data-intensive systems. The Center's main focus is to act as a catalyst towards developing leading-edge algorithms for the capture, storage, processing, analysis and understanding of "data". Data storage and processing require investigation into tools and techniques for modeling, storage, and retrieval. Analysis and understanding require the development of tools and techniques for the processing, symbolic modeling, simulation, and visualization of data. The management and understanding of information has spawned a variety of related disciplines in recent years and the Center for Discovery Informatics consists of participants that reflect this interdisciplinary nature.

Why Discovery Informatics?
As William Agresti points out in his recent article in Communications of the ACM, the world is being flooded in data. Extracting knowledge, information, and relationships from this data is one of the greatest challenges faced by the scientists in the twenty-first century. For example, biologists, environmentalists, economists, retailers, astronomers, and engineers all face the same problem. The data can be unstructured numeric, video, graphics, text, speech, music, etc. The data mining techniques used to analyze the data employ the full range of computing and analytic sciences.

The faculty and research scientists at the Center for Discovery Informatics have a wide range of relevant expertise; databases, pattern recognition, neural networks, genetic algorithms, fuzzy classifiers, computer vision, data mining, complexity theory, etc. The Center for Discovery Informatics provides an environment where researchers and students from across the institute work on interesting problems covering a wide range of disciplines as noted above. Affiliate members collaborate with the researchers on individual projects. Also, the Center's faculty members currently offer a variety of courses in several areas; in particular; Neural Networks and Machine Learning, Computer Vision, Genetic Algorithms, Data Mining, Database Concepts, Advanced Data Management and Pattern Recognition. Ongoing research projects include combined multimedia mining, data management in sensor networks, video information extraction, clustering algorithms, secure databases, etc.

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