Research Faculty
» Pierre Baldi » Wayne Hayes » Ramesh Jain » Chen Li » Sharad Mehrotra » David Newman » Deva Ramanan » Amelia C. Regan » Padhraic Smyth » Max Welling Next-generation database management systems need to be able to provide natural and efficient support for complex multidimensional data sets.
Multidimensional data sets abound in numerous application domains, including: medical information systems that require databases to provide native support for X-rays, volumetric MRI scans and time-varying volumetric information.
Multimedia databases require databases to represent and provide content-based queries over heterogeneous multimedia information such as text, images, video, music, etc.
A primary theme of Bren School research in this area is that of modeling structure in data. Researchers collaborate on data analysis projects with specialists from many different disciplines, including medicine, astronomy, neurology, engineering, chemistry, geology, biology, anthropology, physics and atmospheric science.
The common theme is computationally intensive data analysis allied to the principles of probabilistic inference. This approach incorporates ideas from statistical pattern recognition, applied statistics, data mining, machine learning, theoretical computer science, image and signal analysis, and information theory.
For more information please visit the Center for Machine Learning and Intelligent Systems.
Professor of informatics Crista Lopes (left) and professor of chemical engineering Stanley Grant are developing a web-based watershed information and management tool called the California Sustainable Wetland Information Manager, CalSWIM. 