Emmanuel Candès holds the prestigious Barnum-Simons Chair in Mathematics and Statistics at Stanford University, where he also serves as Professor of Electrical Engineering by courtesy and is a member of the Institute of Computational and Mathematical Engineering. Prior to his current distinguished position, he was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. Dr. Candès completed his PhD in Statistics at Stanford University in 1998, following his Diplôme d'Ingénieur from École Polytechnique in 1993 and a Master of Science from Université de Paris VI. His academic journey began at Stanford from 1998 to 2000 before he joined Caltech's Department of Computing and Mathematical Sciences until 2009, after which he returned to Stanford to establish his influential interdisciplinary research program.
Professor Candès has made transformative contributions to computational harmonic analysis, statistics, information theory, and signal processing through his innovative work in mathematical optimization with profound applications to imaging sciences and scientific computing. His research has established fundamental frameworks for solving inverse problems that have revolutionized approaches to data acquisition and image reconstruction across multiple scientific disciplines. These theoretical advances have found critical real-world applications in medical imaging technologies, enabling faster MRI scans and improved diagnostic capabilities that have enhanced patient care while reducing healthcare costs. His work continues to shape how researchers approach problems in scientific computing, allowing for solutions to previously intractable challenges with remarkable efficiency and mathematical rigor across diverse domains including solid-state physics and computational photography.
Dr. Candès has received numerous prestigious honors including the Alan T. Waterman Award, the highest honor bestowed by the National Science Foundation for early-career scientists, and was elected to both the National Academy of Sciences and the American Academy of Arts and Sciences in 2014. He has delivered over sixty plenary lectures at major international conferences spanning mathematics, statistics, and biomedical imaging, demonstrating his exceptional ability to bridge disciplinary divides. Currently serving as Faculty Director of Stanford Data Science, he plays a pivotal leadership role in shaping the future of interdisciplinary research at the intersection of statistics, computation, and domain sciences. His ongoing research continues to address critical challenges in data science, with recent focus on interpretability, safety, and security in artificial intelligence, positioning him as a key architect of the mathematical foundations guiding responsible innovation in our increasingly data-driven world.
