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| Name |
Candes, Emmanuel J. |
| Location
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Stanford University |
| Primary Field
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Applied Mathematical Sciences |
 Election Citation
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Candes' influential work spans the mathematical sciences, including Harmonic Analysis, Statistical Methodology, Partial Differential Equations, and Optimization. Highly-cited contributions include ridgelet and curvelet bases for image representation, fast wave propagation algorithms, optimal statistical methods exploiting sparsity in high dimensions, and founding new fields of compressed sensing and exact matrix completion. |
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Research Interests
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Emmanuel's work lies at the interface of mathematics, statistics, information theory, signal processing and scientific computing, and is about finding new ways of representing information and of extracting information from complex data. For example, he helped launch the field known as compressed sensing, which is a mathematical technique that has led to advances in the efficiency and accuracy of data collection and analysis, and can be used to significantly speed up MRI scanning times. More broadly, he is interested in theoretical and applied problems characterized by incomplete information. His work combines ideas from probability theory, statistics and mathematical optimization to answer questions such as whether it is possible to recover the phase of a light field from intensity measurements only as in X-ray crystallography; or users' preferences for items from just a few samples as in recommender systems and/or fine details of an object from low-frequency data as in microscopy. His most recent research is concerned with the development of statistical techniques addressing the issue of the irreproducibility of scientific research (the fact that many follow up studies cannot reproduce early findings). |
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