Candás

Candès is a surname of French origin. In contemporary mathematics, statistics, and signal processing, the name is most closely associated with Emmanuel Candès, a French‑American statistician and applied mathematician known for influential work in compressed sensing, low‑rank matrix completion, and robust principal component analysis.

Candès is a professor at Stanford University, with appointments in statistics and electrical engineering and an affiliation with the Institute for Computational and Mathematical Engineering. His research spans high‑dimensional statistics, optimization, and random matrix theory, with a focus on problems involving incomplete or corrupted data. He is recognized for developing theoretical guarantees and practical methods for recovering structured signals and matrices from limited or noisy measurements.

His work has helped establish the foundations of compressed sensing, demonstrating when sparse signals or low‑rank structures can be exactly or robustly recovered via convex optimization from surprisingly few measurements. This line of research has had wide impact across imaging, astronomy, communications, and data science, informing both algorithms and theory. Candès has authored and co‑authored multiple widely cited papers on topics such as exact matrix completion and robust principal component analysis, among others.

Candès has received several honors for his contributions to mathematics and its applications, including a MacArthur Fellowship. His work has shaped both theoretical understanding and practical approaches to data reconstruction in environments with incomplete or corrupted information. The surname Candès may also refer to other individuals bearing the name, but Emmanuel Candès remains the best‑known figure in this context.