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Computer Science
Switzerland
2025

D-Index & Metrics

Computer Science

D-Index
60
Citations
13217
World Ranking
3264
National Ranking
74

Research.com Recognitions

  • 2025 - Research.com Computer Science in Switzerland Leader Award
  • 2022 - Research.com Computer Science in Switzerland Leader Award

Overview

Volkan Cevher is affiliated with the École Polytechnique Fédérale de Lausanne in Switzerland. Their research primarily spans computer science, with substantial focus on artificial intelligence, computational mechanics, management science and operations research, computational theory and mathematics, and numerical analysis.

Cevher's work addresses a variety of specialized topics within these fields including:

  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Advanced Bandit Algorithms Research
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Algorithms
  • Advanced Optimization Algorithms Research
  • Reinforcement Learning in Robotics

Their publication record includes numerous papers, with a majority published on arXiv by Cornell University, alongside contributions in journals such as SIAM Journal on Optimization and IEEE Signal Processing Magazine. Recent papers include:

  • "DiGress: Discrete Denoising diffusion for graph generation," 2022, arXiv (Cornell University)
  • "Lipschitz constant estimation of Neural Networks via sparse polynomial optimization," 2020, arXiv (Cornell University)
  • "On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems," 2020, arXiv (Cornell University)
  • "On the Convergence of Stochastic Primal-Dual Hybrid Gradient," 2022, SIAM Journal on Optimization

Cevher has collaborated frequently with several researchers who have contributed to their body of work. Among these frequent coauthors are:

  • Grigorios G. Chrysos
  • Fanghui Liu
  • Stratis Skoulakis
  • Yongtao Wu
  • Panayotis Mertikopoulos

The scientist's work is disseminated predominantly in the following venues:

  • arXiv (Cornell University)
  • SIAM Journal on Optimization
  • IEEE Signal Processing Magazine
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Mathematical Programming

Volkan Cevher's research continues to contribute to developments in optimization algorithms, machine learning robustness, and computational methods. Their extensive publication record and collaborations reflect ongoing engagement with these areas in computer science and applied mathematics.

Best Publications

  • Model-Based Compressive Sensing

    R.G. Baraniuk;V. Cevher;M.F. Duarte;C. Hegde

  • Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces

    Filiz Yesilkoy;Eduardo R. Arvelo;Yasaman Jahani;Mingkai Liu

  • Compressive Sensing for Background Subtraction

    Volkan Cevher;Aswin Sankaranarayanan;Marco F. Duarte;Dikpal Reddy

  • Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics

    Volkan Cevher;Stephen Becker;Mark W. Schmidt

  • Bilinear Generalized Approximate Message Passing—Part I: Derivation

    Jason T. Parker;Philip Schniter;Volkan Cevher

  • Practical Sketching Algorithms For Low-Rank Matrix Approximation

    Joel A. Tropp;Alp Yurtsever;Madeleine Udell;Volkan Cevher

  • Sparse Signal Recovery Using Markov Random Fields

    Volkan Cevher;Marco F. Duarte;Chinmay Hegde;Richard Baraniuk

  • Distributed target localization via spatial sparsity

    Volkan Cevher;Marco F. Duarte;Richard G. Baraniuk

  • A compressive beamforming method

    A.C. Gurbuz;J.H. McClellan;V. Cevher

  • Low-Dimensional Models for Dimensionality Reduction and Signal Recovery: A Geometric Perspective

    Richard G Baraniuk;Volkan Cevher;Michael B Wakin

  • Convex Optimization for Big Data

    Volkan Cevher;Stephen Becker;Mark Schmidt

  • High-Dimensional Gaussian Process Bandits

    Josip Djolonga;Andreas Krause;Volkan Cevher

  • Scalable Semidefinite Programming

    Alp Yurtsever;Joel Aaron Tropp;Olivier Fercoq;Madeleine Udell

  • WASP: Scalable Bayes via barycenters of subset posteriors

    Sanvesh Srivastava;Volkan Cevher;Quoc Tran Dinh;David B. Dunson

  • Vehicle Speed Estimation Using Acoustic Wave Patterns

    V. Cevher;R. Chellappa;J.H. McClellan

  • Submodular Dictionary Selection for Sparse Representation

    Andreas Krause;Volkan Cevher

  • Learning-Based Compressive MRI

    Baran Gozcu;Rabeeh Karimi Mahabadi;Yen-Huan Li;Efe Ilicak

  • Learning with Compressible Priors

    Volkan Cevher

  • Compressible Distributions for High-Dimensional Statistics

    R. Gribonval;V. Cevher;M. E. Davies

  • Recovery of Clustered Sparse Signals from Compressive Measurements

    Volkan Cevher;Piotr Indyk;Piotr Indyk;Chinmay Hegde;Richard G Baraniuk

  • Randomized Single-View Algorithms for Low-Rank Matrix Approximation

    Joel Aaron Tropp;Alp Yurtsever;Madeleine Udell;Volkan Cevher

  • Supplementary Materials WASP: Scalable Bayes via barycenters of subset posteriors

    Sanvesh Srivastava;Volkan Cevher;Quoc Tran-Dinh;David B. Dunson

  • Low-Dimensional Models for Dimensionality Reduction and Signal Recovery: A Geometric Perspective There are many signal techniques that can be used to perform data acquisition, analysis or processing more efficiently and accurately than can be done by most random signal processors.

    Richard G. Baraniuk;Volkan Cevher;Michael B. Wakin

Frequent Co-Authors

James H. McClellan
James H. McClellan Georgia Institute of Technology
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Hervé Bourlard
Hervé Bourlard Idiap Research Institute
Rama Chellappa
Rama Chellappa Johns Hopkins University
Joel A. Tropp
Joel A. Tropp California Institute of Technology
Lawrence Carin
Lawrence Carin Duke University
Yusuf Leblebici
Yusuf Leblebici École Polytechnique Fédérale de Lausanne
Aswin C. Sankaranarayanan
Aswin C. Sankaranarayanan Carnegie Mellon University
Marco F. Duarte
Marco F. Duarte University of Massachusetts Amherst
Philip Schniter
Philip Schniter The Ohio State University

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