H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 36 Citations 5,153 119 World Ranking 5676 National Ranking 2739

Overview

What is he best known for?

The fields of study he is best known for:

  • Computer network
  • Combinatorics
  • Algorithm

Michael Langberg spends much of his time researching Linear network coding, Discrete mathematics, Combinatorics, Theoretical computer science and Linear code. Michael Langberg interconnects Computational complexity theory, Decoding methods, Distributed computing and Shannon–Fano coding in the investigation of issues within Linear network coding. His study looks at the relationship between Distributed computing and topics such as Wireless network, which overlap with Adversary and Throughput.

His Discrete mathematics research is multidisciplinary, incorporating perspectives in Upper and lower bounds and Combinatorial optimization. In his research on the topic of Theoretical computer science, Telecommunications network, Node and Wireless is strongly related with Multicast. In his study, Robustness is inextricably linked to Coding theory, which falls within the broad field of Network packet.

His most cited work include:

  • Resilient network coding in the presence of Byzantine adversaries (353 citations)
  • Resilient Network Coding in the Presence of Byzantine Adversaries (234 citations)
  • A unified framework for approximating and clustering data (227 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Discrete mathematics, Combinatorics, Linear network coding, Communication channel and Theoretical computer science. The Discrete mathematics study combines topics in areas such as Binary erasure channel, Upper and lower bounds, Binary symmetric channel and Linear code. His Linear network coding study integrates concerns from other disciplines, such as Unicast, Coding and Topology.

His Communication channel research is multidisciplinary, incorporating elements of Encoder and Decoding methods, Code word. His study focuses on the intersection of Theoretical computer science and fields such as Shannon–Fano coding with connections in the field of Tunstall coding. His study on Computer network also encompasses disciplines like

  • Adversary that intertwine with fields like Eavesdropping,
  • Distributed computing, which have a strong connection to Wireless network.

He most often published in these fields:

  • Discrete mathematics (34.42%)
  • Combinatorics (29.77%)
  • Linear network coding (29.30%)

What were the highlights of his more recent work (between 2016-2021)?

  • Communication channel (21.40%)
  • Discrete mathematics (34.42%)
  • Linear network coding (29.30%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Communication channel, Discrete mathematics, Linear network coding, Node and Computer network. His work carried out in the field of Communication channel brings together such families of science as Encoder and Decoding methods. His Discrete mathematics study combines topics from a wide range of disciplines, such as Gilbert–Varshamov bound, Code word, Information theory, Upper and lower bounds and Coding.

His Coding study incorporates themes from Multicast network, Adversary and Eavesdropping. The concepts of his Linear network coding study are interwoven with issues in Theoretical computer science, Shannon–Fano coding, Enhanced Data Rates for GSM Evolution, Network topology and Tunstall coding. His work in the fields of Unicast and Broadcasting overlaps with other areas such as Transmitter and Time rate.

Between 2016 and 2021, his most popular works were:

  • Sufficiently Myopic Adversaries Are Blind (11 citations)
  • The Interplay of Causality and Myopia in Adversarial Channel Models (6 citations)
  • L1-norm principal-component analysis in L2-norm-reduced-rank data subspaces (5 citations)

In his most recent research, the most cited papers focused on:

  • Computer network
  • Statistics
  • Algorithm

Michael Langberg spends much of his time researching Communication channel, Topology, Encoder, Linear network coding and Code. His studies in Communication channel integrate themes in fields like Discrete mathematics and Decoding methods. Michael Langberg has included themes like Binary erasure channel and Information theory in his Discrete mathematics study.

He works mostly in the field of Decoding methods, limiting it down to topics relating to Computer network and, in certain cases, Transmission. While working on this project, Michael Langberg studies both Linear network coding and Rate vector. His studies examine the connections between Code word and genetics, as well as such issues in Upper and lower bounds, with regards to Combinatorics, Limit superior and limit inferior, Quadratic growth and Sequence.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Resilient network coding in the presence of Byzantine adversaries

S. Jaggi;M. Langberg;S. Katti;T. Ho.
ieee international conference computer and communications (2007)

618 Citations

Resilient Network Coding in the Presence of Byzantine Adversaries

S. Jaggi;M. Langberg;S. Katti;T. Ho.
IEEE Transactions on Information Theory (2008)

328 Citations

A unified framework for approximating and clustering data

Dan Feldman;Michael Langberg.
symposium on the theory of computing (2011)

276 Citations

The encoding complexity of network coding

Michael Langberg;Alexander Sprintson;Jehoshua Bruck.
international symposium on information theory (2005)

206 Citations

Approximation Algorithms for Maximization Problems Arising in Graph Partitioning

Uriel Feige;Michael Langberg.
Journal of Algorithms (2001)

164 Citations

On the Hardness of Approximating the Network Coding Capacity

Michael Langberg;Alex Sprintson.
IEEE Transactions on Information Theory (2011)

129 Citations

Universal ε-approximators for integrals

Michael Langberg;Leonard J. Schulman.
symposium on discrete algorithms (2010)

120 Citations

An Equivalence Between Network Coding and Index Coding

Michelle Effros;Salim El Rouayheb;Michael Langberg.
IEEE Transactions on Information Theory (2015)

116 Citations

Realtime classification for encrypted traffic

Roni Bar Yanai;Michael Langberg;David Peleg;Liam Roditty.
symposium on experimental and efficient algorithms (2010)

115 Citations

The RPR 2 rounding technique for semidefinite programs

Uriel Feige;Michael Langberg.
Journal of Algorithms (2006)

90 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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