World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
46
Citations
17497
World Ranking
1312
National Ranking
585

Engineering and Technology

D-Index
48
Citations
17939
World Ranking
4447
National Ranking
1275

Overview

Thomas Strohmer is affiliated with the University of California, Davis in the United States. Their research portfolio primarily spans the field of Computer Science, with a specific focus on Artificial Intelligence, Computational Theory and Mathematics, Statistical and Nonlinear Physics, Computer Science Applications, and Statistics and Probability.

The scientist's work extensively covers key topics such as Privacy-Preserving Technologies in Data, Cryptography and Data Security, Mobile Crowdsensing and Crowdsourcing, Random Matrices and Applications, Complex Network Analysis Techniques, Internet Traffic Analysis and Secure E-voting, and Complexity and Algorithms in Graphs.

The publication record features contributions to various scientific venues, reflecting a strong presence in both preprint and peer-reviewed outlets. Frequent publication venues include:

  • arXiv (Cornell University)
  • SIAM Journal on Mathematics of Data Science
  • Journal of Veterinary Diagnostic Investigation
  • Foundations of Computational Mathematics
  • IEEE Transactions on Information Theory

Thomas Strohmer has collaborated regularly with several researchers. Frequent coauthors include:

  • Roman Vershynin
  • March T. Boedihardjo
  • Shizhou Xu
  • Shaofeng Deng
  • Junda Sheng

Among recent scientific papers, notable works include the following:

  • "Use of machine-learning algorithms to aid in the early detection of leptospirosis in dogs" (2022), published in Journal of Veterinary Diagnostic Investigation
  • "Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data" (2022), published in Foundations of Computational Mathematics
  • "Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data" (2022), published in SIAM Journal on Mathematics of Data Science
  • "Privacy of Synthetic Data: A Statistical Framework" (2022), published in IEEE Transactions on Information Theory
  • "Private measures, random walks, and synthetic data" (2024), published in Probability Theory and Related Fields

Best Publications

  • Grassmannian beamforming for multiple-input multiple-output wireless systems

    D.J. Love;R.W. Heath;T. Strohmer

  • PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming

    Emmanuel J. Candès;Thomas Strohmer;Vladislav Voroninski

  • High-Resolution Radar via Compressed Sensing

    M.A. Herman;T. Strohmer

  • Gabor Analysis and Algorithms: Theory and Applications

    Hans G. Feichtinger;T. Strohmer

  • GRASSMANNIAN FRAMES WITH APPLICATIONS TO CODING AND COMMUNICATION

    Thomas Strohmer;Robert W Heath

  • Phase Retrieval via Matrix Completion

    Emmanuel J. Candès;Yonina C. Eldar;Thomas Strohmer;Vladislav Voroninski

  • A Randomized Kaczmarz Algorithm with Exponential Convergence

    Thomas Strohmer;Roman Vershynin

  • Designing structured tight frames via an alternating projection method

    J.A. Tropp;I.S. Dhillon;R.W. Heath;T. Strohmer

  • General Deviants: An Analysis of Perturbations in Compressed Sensing

    M.A. Herman;T. Strohmer

  • Compressed sensing radar

    M. Herman;T. Strohmer

  • Efficient numerical methods in non-uniform sampling theory

    Hans G. Feichtinger;Karlheinz Gröchenig;Thomas Strohmer

  • Optimal OFDM design for time-frequency dispersive channels

    T. Strohmer;S. Beaver

  • Gabor Analysis and Algorithms

    Hans G. Feichtinger;Thomas Strohmer

  • Advances in Gabor Analysis

    Hans G. Feichtinger;Thomas Strohmer

  • Application of time-reversal with MMSE equalizer to UWB communications

    T. Strohmer;M. Emami;J. Hansen;G. Papanicolaou

  • Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization

    Xiaodong Li;Shuyang Ling;Thomas Strohmer;Ke Wei

  • Self-calibration and biconvex compressive sensing

    Shuyang Ling;Thomas Strohmer

  • Compressed Remote Sensing of Sparse Objects

    Albert C. Fannjiang;Thomas Strohmer;Pengchong Yan

  • Sparse Signal Processing Concepts for Efficient 5G System Design

    Gerhard Wunder;Holger Boche;Thomas Strohmer;Peter Jung

  • Constructing packings in grassmannian manifolds via alternating projection

    Inderjit S. Dhillon;Robert W. Heath;Thomas Strohmer;Joel A. Tropp

Frequent Co-Authors

Robert W. Heath
Robert W. Heath University of California, San Diego
Hans G. Feichtinger
Hans G. Feichtinger University of Vienna
Holger Rauhut
Holger Rauhut RWTH Aachen University
Gitta Kutyniok
Gitta Kutyniok Ludwig-Maximilians-Universität München
Joel A. Tropp
Joel A. Tropp California Institute of Technology
Emmanuel J. Candès
Emmanuel J. Candès Stanford University
Inderjit S. Dhillon
Inderjit S. Dhillon Google (United States)
Benjamin Friedlander
Benjamin Friedlander University of California, Santa Cruz
Arogyaswami Paulraj
Arogyaswami Paulraj Stanford University
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science

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