World's Best Scientists 2026 revealed!

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Computer Science

D-Index
52
Citations
15679
World Ranking
4981
National Ranking
105

Electronics and Electrical Engineering

D-Index
51
Citations
14875
World Ranking
2594
National Ranking
52

Research.com Recognitions

  • 2017 - IEEE Fellow For contributions to network information theory

Overview

Michael Gastpar is affiliated with the École Polytechnique Fédérale de Lausanne in Switzerland. Their research is situated primarily in the field of Computer Science, with a focus spanning several subfields including Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering, Computational Mechanics, and Statistical and Nonlinear Physics.

Their recent publications include the following papers:

  • Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges, 2022, IEEE Journal on Selected Areas in Communications
  • Generalization Error Bounds via Rényi-, f-Divergences and Maximal Leakage, 2021, IEEE Transactions on Information Theory
  • The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources, 2021, IEEE Transactions on Information Theory
  • Compute-Forward for DMCs: Simultaneous Decoding of Multiple Combinations, 2020, IEEE Transactions on Information Theory
  • On Sibson's α-Mutual Information, 2022, 2022 IEEE International Symposium on Information Theory (ISIT)

Gastpar has frequently published in venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Information Theory
  • 2022 IEEE International Symposium on Information Theory (ISIT)
  • IEEE Journal on Selected Areas in Communications
  • Entropy

The scientist has collaborated notably with several coauthors, including:

  • Amedeo Roberto Esposito
  • Marco Bondaschi
  • Ibrahim Issa
  • Erixhen Sula
  • Ashok Vardhan Makkuva

Gastpar's research topics cover a range of areas related to communication technology and information theory. These include:

  • Wireless Communication Security Techniques
  • Distributed Sensor Networks and Detection Algorithms
  • Sparse and Compressive Sensing Techniques
  • Statistical Mechanics and Entropy
  • Neural Networks and Applications
  • Stochastic Gradient Optimization Techniques
  • Cooperative Communication and Network Coding

In recognition of contributions to network information theory, Michael Gastpar was awarded the IEEE Fellow distinction in 2017.

Best Publications

  • Cooperative strategies and capacity theorems for relay networks

    G. Kramer;M. Gastpar;P. Gupta

  • Compute-and-Forward: Harnessing Interference Through Structured Codes

    B. Nazer;M. Gastpar

  • Computation Over Multiple-Access Channels

    B.. Nazer;M.. Gastpar

  • On the capacity of wireless networks: the relay case

    M. Gastpar;M. Vetterli

  • To code, or not to code: lossy source-channel communication revisited

    M. Gastpar;B. Rimoldi;M. Vetterli

  • On Capacity Under Receive and Spatial Spectrum-Sharing Constraints

    M. Gastpar

  • Uncoded transmission is exactly optimal for a simple Gaussian "sensor" network

    M. Gastpar

  • On the capacity of large Gaussian relay networks

    M. Gastpar;M. Vetterli

  • Source-channel communication in sensor networks

    Michael Gastpar;Martin Vetterli

  • Reliable Physical Layer Network Coding

    B Nazer;M Gastpar

  • The Distributed Karhunen–Loève Transform

    M. Gastpar;P.L. Dragotti;M. Vetterli

  • Ergodic Interference Alignment

    B. Nazer;M. Gastpar;S. A. Jafar;S. Vishwanath

  • Integer-Forcing Linear Receivers

    Jiening Zhan;Bobak Nazer;Uri Erez;Michael Gastpar

  • Quantifying high-order interdependencies via multivariate extensions of the mutual information

    Fernando E. Rosas;Pedro A. M. Mediano;Michael Gastpar;Henrik J. Jensen;Henrik J. Jensen

  • Power, spatio-temporal bandwidth, and distortion in large sensor networks

    M. Gastpar;M. Vetterli

  • The Wyner-Ziv problem with multiple sources

    M. Gastpar

  • The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

    G. Reeves;M. Gastpar

  • Sampling bounds for sparse support recovery in the presence of noise

    G. Reeves;M. Gastpar

  • On LP decoding of polar codes

    Naveen Goela;Satish Babu Korada;Michael Gastpar

  • Distributed Source Coding: Theory, Algorithms and Applications

    Pier Luigi Dragotti;Michael Gastpar

  • To code or not to code

    M. Gastpar;B. Rimoldi;M. Vetterli

Frequent Co-Authors

Martin Vetterli
Martin Vetterli École Polytechnique Fédérale de Lausanne
Gerhard Kramer
Gerhard Kramer Technical University of Munich
Kannan Ramchandran
Kannan Ramchandran University of California, Berkeley
Jose M. Carmena
Jose M. Carmena University of California, Berkeley
Pier Luigi Dragotti
Pier Luigi Dragotti Imperial College London
Anant Sahai
Anant Sahai University of California, Berkeley
Changho Suh
Changho Suh Korea Advanced Institute of Science and Technology
Salim El Rouayheb
Salim El Rouayheb Rutgers, The State University of New Jersey
Amos Lapidoth
Amos Lapidoth ETH Zurich
Frédéric E. Theunissen
Frédéric E. Theunissen University of California, Berkeley

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