Emmanuel J. Candès

About

The Barnum-Simons Chair in Mathematics and Statistics at Stanford University, Professor of Mathematics and Statistics, Professor of Electrical Engineering (by courtesy), and Co-chair of the Data Science Institute. Research interests include compressive sensing, mathematical signal processing, computational harmonic analysis, statistics, scientific computing, and applications to the imaging sciences and inverse problems. Other topics of recent interest include theoretical computer science, mathematical optimization, and information theory.

Research topics

• Computer Science
• Biology
• Computational biology
• Machine Learning
• Data Mining
• Statistics
• Mathematics
• Genetics
• Econometrics

Selected publications

• Multi-resolution localization of causal variants across the genome

  Nature Communications · 2020 · 79 citations

  In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial g…

  DOI

• Metropolized Knockoff Sampling

  Journal of the American Statistical Association · 2020 · 66 citations

  Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives. A frequently discussed challenge to apply this method is to construct knockoff variables, which are synthetic variables obeying a crucial exchangeability property with the explanatory variables under study. This article introduces techniques for knockoff generation in grea…

  DOI

• s1: Simple test-time scaling

  2025-01-01 · 33 citations

  articleOpen access

  Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candes, Tatsunori Hashimoto. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

  PublisherOA PDFDOI

• Conformal prediction with conditional guarantees

  Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2025-02-06 · 12 citations

  articleSenior author

  Abstract We consider the problem of constructing distribution-free prediction sets with finite-sample conditional guarantees. Prior work has shown that it is impossible to provide exact conditional coverage universally in finite samples. Thus, most popular methods only guarantee marginal coverage over the covariates or are restricted to a limited set of conditional targets, e.g. coverage over a finite set of prespecified subgroups. This paper bridges this gap by defining a spectrum of problems t…

  PublisherDOI

• Learn then test: Calibrating predictive algorithms to achieve risk control

  The Annals of Applied Statistics · 2025-05-28 · 10 citations

  article

  We introduce a framework for calibrating machine learning models to satisfy finite-sample statistical guarantees. Our calibration algorithms work with any model and (unknown) data-generating distribution and do not require retraining. The algorithms address, among other examples, false discovery rate control in multilabel classification, intersection-over-union control in instance segmentation, and simultaneous control of the type-1 outlier error and confidence set coverage in classification or…

  PublisherDOI

Frequent coauthors

• Rina Foygel Barber

  43 shared

• Ery Arias-Castro

  University of California, San Diego

  37 shared

• Chiara Sabatti

  Stanford University

  29 shared

• Weijie Su

  26 shared

• Małgorzata Bogdan

  University of Wrocław

  23 shared

• David L. Donoho

  Stanford University

  21 shared

• Terence Tao

  University of California, Los Angeles

  20 shared

• Matteo Sesia

  University of Southern California

  20 shared

Education

• B.S., Mathematics

  California Institute of Technology

  1995

• M.S., Mathematics

  California Institute of Technology

  1996

• Ph.D., Mathematics

  California Institute of Technology

  1999

Awards & honors

• 2020 Princess of Asturias Award for Technical and Scientific…
• 2017 MacArthur Fellow
• 2021 IEEE Jack S. Kilby Signal Processing Medal

Similar researchers at Stanford University

• Bradley Efronh-106
• Jerome H. Friedmanh-103