Emmanuel Jean Candès (born 27 April 1970) is a French statistician most well known for his contributions to the field of compressed sensing and statistical hypothesis testing.

In his PhD thesis,[3] he developed generalizations of wavelets called curvelets and ridgelets that were able to capture higher order structures in signals.

[4] In 2006, Candès wrote a paper with Australian-American mathematician Terence Tao[5] that spearheaded the field of compressed sensing: the recovery of sparse signals from a few carefully constructed, and seemingly random measurements.

[2] In 2006, he received the Vasil A. Popov Prize[4] as well as the National Science Foundation's highest honor: the Alan T. Waterman Award for research described by the NSF as "nothing short of revolutionary".

[11] In 2017 Candès received the MacArthur Fellowship for exploring the limits of signal recovery and matrix completion from incomplete data sets with implications for high-impact applications in multiple fields.