H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 83 Citations 29,950 207 World Ranking 376 National Ranking 228

Research.com Recognitions

Awards & Achievements

2019 - ACM Paris Kanellakis Theory and Practice Award For seminal work on the foundations of streaming algorithms and their application to large scale data analytics.

2014 - IEEE Fellow For contributions to parallel computing and databases

2006 - ACM Fellow For contributions to parallel computing, databases, and sensor networks.

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Computer network
  • Algorithm

Phillip B. Gibbons spends much of his time researching Data mining, Parallel computing, Computer network, Distributed computing and Correctness. His Data mining study combines topics from a wide range of disciplines, such as Scheduling, Theoretical computer science and Set. His Parallel computing research includes themes of Dram, Hard disk drive performance characteristics and Distributed shared memory.

His work deals with themes such as Computer security, Alias and The Internet, which intersect with Computer network. He combines subjects such as Sequential consistency, Distributed memory, Shared memory, Cache coherence and Uniform memory access with his study of Distributed computing. His study in Correctness is interdisciplinary in nature, drawing from both Model of computation, Interface, Consistency model, Asynchronous communication and Robustness.

His most cited work include:

  • Memory consistency and event ordering in scalable shared-memory multiprocessors (1066 citations)
  • SybilGuard: defending against sybil attacks via social networks (748 citations)
  • LOCI: fast outlier detection using the local correlation integral (681 citations)

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

His primary areas of investigation include Parallel computing, Distributed computing, Cache, Parallel algorithm and Algorithm. His Parallel computing study incorporates themes from Dram, Scheduling and Asynchronous communication. In his research on the topic of Distributed computing, Protocol is strongly related with Key.

He regularly links together related areas like Speedup in his Cache studies. His research integrates issues of Discrete mathematics and Computation in his study of Parallel algorithm. His research in Shared memory focuses on subjects like Uniform memory access, which are connected to Interleaved memory and Memory map.

He most often published in these fields:

  • Parallel computing (32.49%)
  • Distributed computing (16.25%)
  • Cache (15.52%)

What were the highlights of his more recent work (between 2014-2021)?

  • Parallel computing (32.49%)
  • Artificial intelligence (5.78%)
  • Cache (15.52%)

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

Parallel computing, Artificial intelligence, Cache, Distributed computing and Algorithm are his primary areas of study. His studies in Parallel computing integrate themes in fields like Dram and Compiler. His work in Artificial intelligence covers topics such as Machine learning which are related to areas like Key, Data modeling, Software system and Software deployment.

His research in Cache intersects with topics in Load balancing and Work stealing. His Distributed computing research integrates issues from Scratchpad memory, Porting, Programmer, Exploit and Programming paradigm. His study in the fields of Computation and Parallel algorithm under the domain of Algorithm overlaps with other disciplines such as Estimation.

Between 2014 and 2021, his most popular works were:

  • Advances and Open Problems in Federated Learning (571 citations)
  • Ambit: in-memory accelerator for bulk bitwise operations using commodity DRAM technology (195 citations)
  • GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server (192 citations)

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

  • Operating system
  • Programming language
  • Algorithm

Phillip B. Gibbons mostly deals with Parallel computing, Cache, Artificial intelligence, Key and Distributed computing. His research in the fields of Pipeline overlaps with other disciplines such as Throughput. His Pipeline research focuses on Range and how it relates to Computation.

His study in Cache is interdisciplinary in nature, drawing from both Memory-mapped file, Memory management and Shared memory. His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Normalization, Degree, Skew and Local area network. His research in Distributed computing intersects with topics in Elasticity, Reliability and Transient.

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

Memory consistency and event ordering in scalable shared-memory multiprocessors

Kourosh Gharachorloo;Daniel Lenoski;James Laudon;Phillip Gibbons.
international symposium on computer architecture (1990)

1714 Citations

LOCI: fast outlier detection using the local correlation integral

S. Papadimitriou;H. Kitagawa;P.B. Gibbons;C. Faloutsos.
international conference on data engineering (2003)

1043 Citations

SybilGuard: defending against sybil attacks via social networks

Haifeng Yu;Michael Kaminsky;Phillip B. Gibbons;Abraham D. Flaxman.
acm special interest group on data communication (2006)

1033 Citations

SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks

Haifeng Yu;Phillip B. Gibbons;Michael Kaminsky;Feng Xiao.
ieee symposium on security and privacy (2008)

849 Citations

Synopsis diffusion for robust aggregation in sensor networks

Suman Nath;Phillip B. Gibbons;Srinivasan Seshan;Zachary Anderson.
ACM Transactions on Sensor Networks (2008)

821 Citations

IrisNet: an architecture for a worldwide sensor Web

P.B. Gibbons;B. Karp;Y. Ke;S. Nath.
IEEE Pervasive Computing (2003)

628 Citations

New sampling-based summary statistics for improving approximate query answers

Phillip B. Gibbons;Yossi Matias.
international conference on management of data (1998)

593 Citations

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

Qirong Ho;James Cipar;Henggang Cui;Seunghak Lee.
neural information processing systems (2013)

499 Citations

Join synopses for approximate query answering

Swarup Acharya;Phillip B. Gibbons;Viswanath Poosala;Sridhar Ramaswamy.
international conference on management of data (1999)

437 Citations

System and method for scheduling and controlling delivery of advertising in a communications network

Micah Alexei Adler;Phillip B. Gibbons;Yossi Matias.
(1997)

412 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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