World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
45
Citations
11454
World Ranking
3472
National Ranking
1281

Computer Science

D-Index
55
Citations
13533
World Ranking
4276
National Ranking
2016

Michael Rabbat publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Michael Rabbat sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 157 publications — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Michael Rabbat D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Michael Rabbat sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 45 D-Index — 50th percentile

50% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Overview

Michael Rabbat is affiliated with Facebook in the United States and conducts research primarily in the field of Computer Science. Their work spans several subfields, with a significant focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications, and Computational Mechanics.

The scientist's research topics are diverse and include:

  • Domain Adaptation and Few-Shot Learning
  • Stochastic Gradient Optimization Techniques
  • Privacy-Preserving Technologies in Data
  • Multimodal Machine Learning Applications
  • Advanced Image and Video Retrieval Techniques
  • Sparse and Compressive Sensing Techniques
  • Advanced Neural Network Applications

Michael Rabbat has contributed to over 67 publications in Computer Science, with frequent appearances in credible venues such as:

  • arXiv (Cornell University)
  • Proceedings of the IEEE
  • Radiology Artificial Intelligence
  • Magnetic Resonance in Medicine
  • American Journal of Roentgenology

Among their recent publications are:

  • DINOv2: Learning Robust Visual Features without Supervision, 2023, arXiv (Cornell University)
  • fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning, 2020, Radiology Artificial Intelligence
  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge, 2020, Magnetic Resonance in Medicine
  • Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study, 2020, American Journal of Roentgenology
  • Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Collaboration is also a notable aspect of their research as evidenced by frequent co-authors including:

  • Mahmoud Assran
  • Nicolas Ballas
  • Ishan Misra
  • Piotr Bojanowski
  • Armand Joulin

This collaborative network indicates sustained partnerships with other researchers across various projects and publications.

Best Publications

  • Distributed optimization in sensor networks

    Michael Rabbat;Robert Nowak

  • Gossip Algorithms for Distributed Signal Processing

    Alexandros G Dimakis;Soummya Kar;José M F Moura;Michael G Rabbat

  • Compressed Sensing for Networked Data

    J. Haupt;W.U. Bajwa;M. Rabbat;R. Nowak

  • fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.

    Jure Zbontar;Florian Knoll;Anuroop Sriram;Matthew J. Muckley

  • How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal

    Ahmadreza Faghih-Imani;Naveen Eluru;Ahmed M. El-Geneidy;Michael Rabbat

  • Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization

    Angelia Nedic;Alex Olshevsky;Michael G. Rabbat

  • Quantized incremental algorithms for distributed optimization

    M.G. Rabbat;R.D. Nowak

  • Learning Graphs From Data: A Signal Representation Perspective

    Xiaowen Dong;Dorina Thanou;Michael Rabbat;Pascal Frossard

  • fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.

    Florian Knoll;Jure Zbontar;Anuroop Sriram;Matthew J Muckley

  • Distributed Average Consensus With Dithered Quantization

    T.C. Aysal;M.J. Coates;M.G. Rabbat

  • Sustainable AI: Environmental Implications, Challenges and Opportunities.

    Carole-Jean Wu;Ramya Raghavendra;Udit Gupta;Bilge Acun

  • Push-Sum Distributed Dual Averaging for convex optimization

    Konstantinos I. Tsianos;Sean Lawlor;Michael G. Rabbat

  • Decentralized source localization and tracking [wireless sensor networks]

    M.G. Rabbat;R.D. Nowak

  • Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning

    Konstantinos I. Tsianos;Sean Lawlor;Michael G. Rabbat

  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

    Florian Knoll;Tullie Murrell;Anuroop Sriram;Nafissa Yakubova

  • Decentralized compression and predistribution via randomized gossiping

    Michael Rabbat;Jarvis Haupt;Aarti Singh;Robert Nowak

  • Decentralized source localization and tracking

    Michael G. Rabbat;Robert D. Nowak

  • A Graph-CNN for 3D Point Cloud Classification

    Yingxue Zhang;Michael Rabbat

  • Stochastic Gradient Push for Distributed Deep Learning

    Mahmoud Assran;Nicolas Loizou;Nicolas Ballas;Michael G. Rabbat

  • Approximating signals supported on graphs

    Xiaofan Zhu;Michael Rabbat

  • SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

    Jianyu Wang;Vinayak Tantia;Nicolas Ballas;Michael Rabbat

Frequent Co-Authors

Mark Coates
Mark Coates McGill University
Robert Nowak
Robert Nowak University of Wisconsin–Madison
Nicolas Ballas
Nicolas Ballas Facebook (United States)
Mikael Johansson
Mikael Johansson Royal Institute of Technology
C. Lawrence Zitnick
C. Lawrence Zitnick Facebook (United States)
Florian Knoll
Florian Knoll University of Erlangen-Nuremberg
Warren J. Gross
Warren J. Gross McGill University
Alejandro Ribeiro
Alejandro Ribeiro University of Pennsylvania
Carlo Fischione
Carlo Fischione Royal Institute of Technology

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