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Affiliated Faculty Member:
Center for Information and Systems Engineering
Center for Nanoscience and Nanobiotechnology
Center for Space Physics
Division of Systems Engineering
Biography
William Clem Karl received the Ph.D. degree in Electrical Engineering and Computer Science in 1991 from the Massachusetts Institute of Technology, Cambridge,
where he also received the S.M., E.E., and S.B. degrees. He held the position of Staff Research Scientist with the Brown-Harvard-M.I.T. Center for Intelligent Control Systems and the M.I.T. Laboratory for
Information and Decision Systems from 1992 to 1994. He joined the faculty of Boston University in 1995, where he is currently Professor of Electrical and Computer Engineering and Biomedical Engineering.
He has been an Associate Editor of the IEEE Transactions on Image Processing and was the General Chair of the 2009 IEEE International Symposium on Biomedical Imaging. He is currently a member of the Board of Governors of the IEEE Signal Processing Society, the Signal Processing Society Conference Board, and the IEEE Transactions on Medical Imaging Steering Committee. He is a past member of the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee and is a current member of the IEEE Biomedical Image and Signal Processing Technical Committee. Dr. Karl's research interests are in the areas of multidimensional statistical signal and image processing, estimation, inverse problems, geometric estimation, and applications to problems ranging from biomedical signal and image processing to synthetic aperture radar.
Curriculum Vitae
Academic genealogy
Research
Prof. Karl’s current research interests cover the following areas:
- Multidimensional statistical signal and image processingl
- Detection and estimation, high-dimensional inference
- Inverse problems, tomography
- Geometric estimation
- Biological and medical signal and image processing
Prof. Karl is interested in the general areas of multidimensional and multiresolution signal and image processing and estimation and geometric-based
esimtaion. The projects that motivate this research include, but are not limited to, problems arising in automatic target detection and recognition (synthetic aperture radar), geophysical inverse problems (such as finding oil and analyzing the atmosphere), and medical estimation problems (such as tomography and MRI). The general goal is to develop efficient methods for the extraction of information from diverse data sources in the presence of uncertainty. The approach taken is based on the development of statistical models for both observations and prior knowledge and the subsequent use of these models for optimal or near-optimal processing.
Prospective students: If you are interested in the above research areas, and have a strong engineering background, I recommend that you apply to our graduate program. Please do not
send me emails with your resume; we only consider students that submit a full application.
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