World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
40
Citations
9872
World Ranking
2000
National Ranking
127

Overview

Holger Rauhut is affiliated with RWTH Aachen University in Germany and specializes in research spanning computer science and engineering. Their contributions include work in several subfields such as artificial intelligence, computational mechanics, computer vision and pattern recognition, signal processing, and biomedical engineering.

The main topics covered in Rauhut's research include:

  • Sparse and Compressive Sensing Techniques
  • Image and Signal Denoising Methods
  • Blind Source Separation Techniques
  • Stochastic Gradient Optimization Techniques
  • Neural Networks and Applications
  • Photoacoustic and Ultrasonic Imaging
  • Gaussian Processes and Bayesian Inference

Their recent publications exemplify this range of interests. Selected papers include:

  • "Covariance estimation under one-bit quantization" (2022), published in The Annals of Statistics
  • "ADMM-DAD Net: A Deep Unfolding Network for Analysis Compressed Sensing" (2022), presented at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Tensor theta norms and low rank recovery" (2020), published in Numerical Algorithms
  • "Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers" (2021), published in Information and Inference A Journal of the IMA
  • "Prediction of the disease course in Friedreich ataxia" (2022), published in Scientific Reports

Frequent coauthors in Rauhut's research network include Johannes Maly, Hung-Hsu Chou, Sjoerd Dirksen, Felix Krahmer, and Vicky Kouni.

They have published extensively in venues such as arXiv (Cornell University), Information and Inference A Journal of the IMA, Advances in Continuous and Discrete Models, Applied and Computational Harmonic Analysis, and SSRN Electronic Journal.

Holger Rauhut's body of work integrates theoretical and applied methods, particularly in the development and analysis of algorithms for signal processing, compressed sensing, and neural network learning. Their research contributes to the understanding and advancement of efficient methods in handling complex data and computational challenges within engineering and computer science contexts.

Best Publications

  • A Mathematical Introduction to Compressive Sensing

    Simon Foucart;Holger Rauhut

  • Compressed Sensing and Redundant Dictionaries

    H. Rauhut;K. Schnass;P. Vandergheynst

  • Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation

    Y.C. Eldar;H. Rauhut

  • Compressive Estimation of Doubly Selective Channels in Multicarrier Systems: Leakage Effects and Sparsity-Enhancing Processing

    G. Taubock;F. Hlawatsch;D. Eiwen;H. Rauhut

  • Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints

    Massimo Fornasier;Holger Rauhut

  • Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization

    Massimo Fornasier;Holger Rauhut;Rachel Ward

  • Iterative thresholding algorithms

    Massimo Fornasier;Holger Rauhut

  • Suprema of Chaos Processes and the Restricted Isometry Property

    Felix Krahmer;Shahar Mendelson;Holger Rauhut

  • Sparse Legendre expansions via l 1 -minimization

    Holger Rauhut;Rachel Ward

  • Restricted isometries for partial random circulant matrices

    Holger Rauhut;Justin K. Romberg;Joel A. Tropp

  • Atoms of All Channels, Unite! Average Case Analysis of Multi-Channel Sparse Recovery Using Greedy Algorithms

    Rémi Gribonval;Holger Rauhut;Karin Schnass;Pierre Vandergheynst

  • Random Sampling of Sparse Trigonometric Polynomials, II. Orthogonal Matching Pursuit versus Basis Pursuit

    Stefan Kunis;Holger Rauhut

  • Low rank matrix recovery from rank one measurements

    Richard Kueng;Holger Rauhut;Ulrich Terstiege

  • Continuous Frames, Function Spaces, and the Discretization Problem

    Massimo Fornasier;Holger Rauhut

  • Random Sampling of Sparse Trigonometric Polynomials

    Holger Rauhut

  • An Invitation to Compressive Sensing

    Simon Foucart;Holger Rauhut

  • Low rank tensor recovery via iterative hard thresholding

    Holger Rauhut;Reinhold Schneider;Zeljka Stojanac;Zeljka Stojanac

  • Interpolation via weighted ℓ1 minimization

    Holger Rauhut;Rachel A Ward

  • The Gelfand widths of l p -balls for 0<p≤1

    Simon Foucart;Alain Pajor;Holger Rauhut;Tino Ullrich

  • Circulant and Toeplitz Matrices in Compressed Sensing

    Holger Rauhut

  • The restricted isometry property for time-frequency structured random matrices

    Goetz E Pfander;Holger Rauhut;Joel A Tropp

  • Interpolation via weighted $l_1$ minimization

    Holger Rauhut;Rachel Ward

Frequent Co-Authors

Rachel Ward
Rachel Ward The University of Texas at Austin
Massimo Fornasier
Massimo Fornasier Technical University of Munich
Gitta Kutyniok
Gitta Kutyniok Ludwig-Maximilians-Universität München
Pierre Vandergheynst
Pierre Vandergheynst École Polytechnique Fédérale de Lausanne
Thomas Strohmer
Thomas Strohmer University of California, Davis
Shahar Mendelson
Shahar Mendelson Texas A&M University
Joel A. Tropp
Joel A. Tropp California Institute of Technology
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science

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