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Computer Science

D-Index
42
Citations
9910
World Ranking
8249
National Ranking
3537

Overview

Rebecca Willett is a researcher affiliated with the University of Chicago in the United States. Their work spans various areas within computer science, with a strong emphasis on artificial intelligence and its applications across interdisciplinary fields.

The primary fields of study in Rebecca Willett's research include:

  • Computer Science

Within this broad discipline, their research focuses on several subfields, notably:

  • Artificial Intelligence
  • Molecular Biology
  • Computer Vision and Pattern Recognition
  • Computational Mechanics
  • Biomedical Engineering

The main research topics covered by Rebecca Willett's work are:

  • Sparse and Compressive Sensing Techniques
  • Neural Networks and Applications
  • Meteorological Phenomena and Simulations
  • Medical Imaging Techniques and Applications
  • Climate Variability and Models
  • Model Reduction and Neural Networks
  • Statistical Methods and Inference

Rebecca Willett has co-authored publications frequently with several scholars in the field, including:

  • Greg Ongie
  • Daniel Sanz-Alonso
  • Daren Wang
  • Rina Foygel Barber
  • Jake A. Soloff

The venues where Rebecca Willett has often published their research include:

  • arXiv (Cornell University)
  • SIAM Journal on Mathematics of Data Science
  • bioRxiv (Cold Spring Harbor Laboratory)
  • IEEE Journal on Selected Areas in Information Theory
  • Nature Communications

Recent notable papers include:

  • "Deep Learning Techniques for Inverse Problems in Imaging," published in 2020 in the IEEE Journal on Selected Areas in Information Theory
  • "An optimal statistical and computational framework for generalized tensor estimation," published in 2022 in The Annals of Statistics
  • "Climate-driven changes in the predictability of seasonal precipitation," published in 2023 in Nature Communications
  • "Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations," published in 2020 in Atmospheric Measurement Techniques
  • "Autodifferentiable Ensemble Kalman Filters," published in 2022 in SIAM Journal on Mathematics of Data Science

The scope of Rebecca Willett's research encompasses the development and application of computational and statistical methods aimed at solving complex problems in imaging, climate science, and biological contexts. Their work integrates advances in neural networks and model reduction techniques with efforts addressing meteorological variability and medical imaging challenges.

Best Publications

  • Single disperser design for coded aperture snapshot spectral imaging

    Ashwin Wagadarikar;Renu John;Rebecca Willett;David Brady

  • Single-shot compressive spectral imaging with a dual-disperser architecture

    M. E. Gehm;R. John;D. J. Brady;R. M. Willett

  • Deep Learning Techniques for Inverse Problems in Imaging

    Gregory Ongie;Ajil Jalal;Christopher A. Metzler;Richard G. Baraniuk

  • This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice

    Z. T. Harmany;R. F. Marcia;R. M. Willett

  • Poisson Noise Reduction with Non-local PCA

    Joseph Salmon;Zachary Harmany;Charles-Alban Deledalle;Rebecca Willett

  • Platelets: a multiscale approach for recovering edges and surfaces in photon-limited medical imaging

    R.M. Willett;R.D. Nowak

  • Backcasting: adaptive sampling for sensor networks

    Rebecca Willett;Aline Martin;Robert Nowak

  • Poisson noise reduction with non-local PCA

    J. Salmon;C-A. Deledalle;R. Willett;Z. Harmany

  • Compressed sensing for practical optical imaging systems: a tutorial

    Rebecca M. Willett;Roummel F. Marcia;Jonathan M. Nichols

  • Online Convex Optimization in Dynamic Environments

    Eric C. Hall;Rebecca M. Willett

  • Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

    Rebecca M. Willett;Marco F. Duarte;Mark A. Davenport;Richard G. Baraniuk

  • Compressed Sensing Performance Bounds Under Poisson Noise

    M Raginsky;R M Willett;Z T Harmany;R F Marcia

  • Faster Rates in Regression via Active Learning

    Rebecca Willett;Robert Nowak;Rui M. Castro

  • Compressive coded aperture superresolution image reconstruction

    R.F. Marcia;R.M. Willett

  • Multiscale Poisson Intensity and Density Estimation

    R.M. Willett;R.D. Nowak

  • Change-Point Detection for High-Dimensional Time Series With Missing Data

    Yao Xie;Jiaji Huang;R. Willett

  • Estimating inhomogeneous fields using wireless sensor networks

    R. Nowak;U. Mitra;R. Willett

  • Neumann Networks for Linear Inverse Problems in Imaging

    Davis Gilton;Greg Ongie;Rebecca Willett

  • Compressive coded aperture imaging

    Roummel F. Marcia;Zachary T. Harmany;Rebecca M. Willett

  • Compressive coded aperture video reconstruction

    Roummel F. Marcia;Rebecca M. Willett

  • A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case

    Greg Ongie;Rebecca Willett;Daniel Soudry;Nathan Srebro

Frequent Co-Authors

Robert Nowak
Robert Nowak University of Wisconsin–Madison
David J. Brady
David J. Brady University of Arizona
Stephen J. Wright
Stephen J. Wright University of Wisconsin–Madison
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Clayton Scott
Clayton Scott University of Michigan–Ann Arbor
Robert E. Holz
Robert E. Holz University of Wisconsin–Madison
Francesco Orabona
Francesco Orabona King Abdullah University of Science and Technology
Svetlana Lazebnik
Svetlana Lazebnik University of Illinois at Urbana-Champaign
Jungsang Kim
Jungsang Kim Duke University
Ian Foster
Ian Foster University of Chicago

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