معرفی
Emmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, where he is also a Professor of Statistics and, by courtesy, of Electrical Engineering. He is a member of the Institute of Computational and Mathematical Engineering at Stanford. Previously, he was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology.
His educational background includes:
- PhD in Statistics from Stanford University (1998)
- Diplome Ingenieur from Ecole Polytechnique (1993)
Candès' research spans computational harmonic analysis, statistics, information theory, signal processing, and mathematical optimization with applications to imaging sciences, scientific computing, and inverse problems. His recent work focuses on conformal prediction, uncertainty quantification, and causal inference, with applications across diverse fields including genetics, machine learning, and artificial intelligence. He has made significant contributions to compressive sensing and mathematical signal processing.
His recent publications demonstrate a strong focus on developing statistically rigorous methods for uncertainty quantification, particularly through conformal prediction frameworks. These works address challenges in high-dimensional statistics, machine learning validation, and causal inference across various application domains including genomics, natural language processing, and imaging sciences.
His notable scientific achievements include:
- Alan T. Waterman Award from NSF
- IEEE Jack S. Kilby Signal Processing Medal (2021)
- Princess of Asturias Award for Technical and Scientific Research (2020)
- MacArthur Fellow (2017)
- Election to the National Academy of Sciences (2014)
- Election to the American Academy of Arts and Sciences (2014)
Candès has served as Chair of the Statistics Department at Stanford (2016-2019) and is currently serving as Director of the Data Science Institute. His research has been supported by numerous grants from the National Science Foundation and other funding agencies. He has given over 60 plenary lectures at major international conferences across mathematics, statistics, biomedical imaging, and physics.
As a leading researcher in mathematical statistics and computational mathematics, Candès maintains an active research group focusing on theoretical and applied aspects of statistical learning, signal processing, and optimization. His work bridges theoretical foundations with practical applications across scientific disciplines.
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