The Shaw Prize in Mathematical Sciences 2026 is awarded in equal shares to Emmanuel Candès, the Barnum–Simons Chair in Mathematics and Statistics, Stanford University, USA and Camillo De Lellis, the IBM von Neumann Professor, School of Mathematics, Institute for Advanced Study, USA for their breakthrough contributions to the use of deep techniques from mathematical analysis to rigorously understand applied problems in information theory, signal processing and statistics on the one hand, and to the study of singularities in geometric measure theory and fluid dynamics on the other.

Emmanuel Candès has made seminal contributions at the intersection of pure and applied mathematics, using mathematical analysis to provide clear, rigorous and useful mathematical frameworks to everyday practical problems in information processing.

A major contribution of Candès (with Justin Romberg, Terence Tao, and David Donoho) is the development of compressed sensing, a new mathematical framework for reconstructing signals from incomplete measurements. The work has proven enormously impactful in signal processing, medical imaging, and statistics. The key insight was that one can massively undersample, and despite so much missing data, still reconstruct. A non-commutative analogue of compressed sensing (developed with Benjamin Recht and Tao) allowed Candès to reconstruct low-rank matrices from partial entries. This work had significant impact in machine learning and data science. Candès (with his student Carlos Fernandez-Granda) developed a mathematical theory of superresolution, allowing one to reconstruct high-resolution signals from low-resolution measurements. The approach used by Candès connected this problem with classical work in mathematical analysis by Beurling in his theory of minimal extrapolation.

Candès (with his postdoc Rina Foygel Barber) introduced a new filter that can reduce the false recovery rate in statistics, which has the potential to have the same impact in statistics as compressed sensing has had in recent years in image processing. Compressed sensing transformed signal processing by reconstructing from small amounts of data.

Emmanuel Candès was born in 1970 in Paris, France and is currently the Barnum–Simons Chair in Mathematics and Statistics, Stanford University, USA. He received his Bachelor’s degree from the École Polytechnique, France in 1993, and a PhD in Statistics from Stanford University in 1998. After serving as Assistant Professor of Statistics at Stanford (1998–2000), he joined the California Institute of Technology in 2000, where he was successively appointed as Assistant Professor, Associate Professor, Professor from 2000 to 2006, as well as Ronald and Maxine Linde Professor of Applied and Computational Mathematics (2006–11). He returned to Stanford in 2009 as Professor of Mathematics and Statistics. He was chair of the Department of Statistics (2016–18) and has been the Barnum–Simons Chair in Mathematics and Statistics since 2012. He is a member of the US National Academy of Sciences and the American Academy of Arts and Sciences.