World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
13533
World Ranking
4276
National Ranking
2015

Electronics and Electrical Engineering

D-Index
45
Citations
11454
World Ranking
3472
National Ranking
1280

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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