Emmanuel Candès is a mathematician and statistician known for developing a unified framework for addressing a range of problems in engineering and computer science, most notably compressed sensing. Compressed sensing is a technique for efficiently reconstructing or acquiring signals that make up sounds and images. Candès's research focuses on reconstructing high-resolution images from small numbers of random measurements, as well as recovering the missing entries in massive data tables.
Candès and colleagues were able to reconstruct high-resolution signals from sparse measurements under specified conditions. In diagnostic healthcare, for example, reducing the number of measurements needed to create high-resolution MRI scans shortens a number of time patients must remain still in the scanner, an outcome with particularly beneficial implications for children. The ability to process and/or reconstruct audio, visual and wireless signals from limited data has also led to significant refinements in digital photography, radar imaging, and wireless communications.
His work at the interface of applied and theoretical mathematics is generating new lines of research in information theory as well as laying the groundwork for improvements in many devices that make use of signal and image processing methods.
Candès received a B.E. (1993) from École Polytechnique, an M.Sc. (1994) from Université de Paris VI, and a Ph.D. (1998) from Stanford University. He was a member of the faculty of Stanford University (1998–2000) and the Department of Computing and Mathematical Sciences at the California Institute of Technology (2000–2009), before returning to Stanford as the Barnum-Simons Chair in Mathematics and Statistics in the Departments of Mathematics and Statistics and a professor of electrical engineering. In 2017, he was named a MacArthur 'Genius Grant' Fellow.
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