D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 53 Citations 14,444 138 World Ranking 2385 National Ranking 137

Overview

What is he best known for?

The fields of study he is best known for:

  • Quantum mechanics
  • Algorithm
  • Artificial intelligence

His primary areas of investigation include Probabilistic logic, Theoretical computer science, Model checking, Algorithm and Formal verification. His Probabilistic logic research includes themes of Automaton, Temporal logic, Prism, Markov decision process and Nondeterministic algorithm. His Temporal logic research incorporates elements of Correctness and Markov chain.

His Theoretical computer science study combines topics in areas such as Range, Probabilistic CTL, Statistical model and Extension. His work in the fields of Abstraction model checking and PRISM model checker overlaps with other areas such as Bluetooth. The various areas that David Parker examines in his Algorithm study include Property, Probabilistic analysis of algorithms and Divergence-from-randomness model.

His most cited work include:

  • PRISM 4.0: verification of probabilistic real-time systems (1627 citations)
  • PRISM : A tool for automatic verification of probabilistic systems (571 citations)
  • PRISM: Probabilistic Symbolic Model Checker (557 citations)

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

The scientist’s investigation covers issues in Probabilistic logic, Theoretical computer science, Particle, Mechanics and Tracking. His Probabilistic logic research is multidisciplinary, incorporating elements of Temporal logic, Model checking, Formal verification, Algorithm and Markov decision process. The Model checking study combines topics in areas such as Mathematical optimization and Binary decision diagram.

David Parker has researched Theoretical computer science in several fields, including Probabilistic CTL, Markov process, Markov chain and Statistical model. His work carried out in the field of Particle brings together such families of science as Granular material and Mineralogy. His Tracking study combines topics from a wide range of disciplines, such as TRACER, Optics and Positron emission.

He most often published in these fields:

  • Probabilistic logic (28.64%)
  • Theoretical computer science (18.84%)
  • Particle (18.59%)

What were the highlights of his more recent work (between 2015-2021)?

  • Probabilistic logic (28.64%)
  • Condensed matter physics (5.53%)
  • Markov decision process (12.06%)

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

His scientific interests lie mostly in Probabilistic logic, Condensed matter physics, Markov decision process, Theoretical computer science and Distributed computing. His studies deal with areas such as Temporal logic, Model checking, Formal verification, Algorithm and Markov chain as well as Probabilistic logic. His biological study spans a wide range of topics, including Property, Cryptographic protocol and Markov process.

He combines subjects such as Linear programming and Control theory with his study of Markov decision process. His specific area of interest is Theoretical computer science, where David Parker studies Nondeterministic algorithm. His research integrates issues of Key, Task and Benchmark in his study of Distributed computing.

Between 2015 and 2021, his most popular works were:

  • Quantitative Verification and Synthesis of Attack-Defence Scenarios (44 citations)
  • New Measurement of the Direct 3 α Decay from the C 12 Hoyle State (41 citations)
  • PRISM-games: verification and strategy synthesis for stochastic multi-player games with multiple objectives. (36 citations)

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

  • Quantum mechanics
  • Artificial intelligence
  • Programming language

David Parker spends much of his time researching Probabilistic logic, Distributed computing, Formal verification, Theoretical computer science and Model checking. His work deals with themes such as Control theory, Markov decision process, Markov chain, Benchmark and Software engineering, which intersect with Probabilistic logic. His research in Distributed computing intersects with topics in Task, Mobile robot, Real-time computing, Bounded function and Key.

David Parker interconnects Observability, Rotation formalisms in three dimensions, State and Energy management in the investigation of issues within Formal verification. His Theoretical computer science research includes themes of Algorithm, Stochastic modelling and Subgame perfect equilibrium. His Model checking study integrates concerns from other disciplines, such as Property and Markov process.

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.

Best Publications

PRISM 4.0: verification of probabilistic real-time systems

Marta Kwiatkowska;Gethin Norman;David Parker.
computer aided verification (2011)

2337 Citations

PRISM: a tool for automatic verification of probabilistic systems

Andrew Hinton;Marta Kwiatkowska;Gethin Norman;David Parker.
tools and algorithms for construction and analysis of systems (2006)

928 Citations

PRISM: Probabilistic Symbolic Model Checker

Marta Z. Kwiatkowska;Gethin Norman;David Parker.
Lecture Notes in Computer Science (2002)

815 Citations

Stochastic model checking

Marta Kwiatkowska;Gethin Norman;David Parker.
formal methods (2007)

678 Citations

Adsorption kinetics of fluoride on low cost materials

X Fan;D J Parker;Smith.
Water Research (2003)

676 Citations

Probabilistic Symbolic Model Checking with PRISM: A Hybrid Approach

Marta Z. Kwiatkowska;Gethin Norman;David Parker.
tools and algorithms for construction and analysis of systems (2004)

417 Citations

Automated Verification Techniques for Probabilistic Systems

Vojtech Forejt;Marta Z. Kwiatkowska;Gethin Norman;David Parker.
formal methods (2011)

322 Citations

PRISM: probabilistic model checking for performance and reliability analysis

Marta Kwiatkowska;Gethin Norman;David Parker.
measurement and modeling of computer systems (2009)

291 Citations

Positron emission particle tracking studies of spherical particle motion in rotating drums

D.J. Parker;A.E. Dijkstra;T.W. Martin;J.P.K. Seville.
Chemical Engineering Science (1997)

286 Citations

Commonality, Difference and the Dynamics of Disclosure in In-Depth Interviewing

Miri Song;David Parker.
Sociology (1995)

279 Citations

Best Scientists Citing David Parker

Joost-Pieter Katoen

Joost-Pieter Katoen

RWTH Aachen University

Publications: 149

Marta Kwiatkowska

Marta Kwiatkowska

University of Oxford

Publications: 105

Holger Hermanns

Holger Hermanns

Saarland University

Publications: 102

Christel Baier

Christel Baier

TU Dresden

Publications: 89

Axel Legay

Axel Legay

Université Catholique de Louvain

Publications: 85

Krishnendu Chatterjee

Krishnendu Chatterjee

Institute of Science and Technology Austria

Publications: 77

Alessandro Abate

Alessandro Abate

University of Oxford

Publications: 57

Jane Hillston

Jane Hillston

University of Edinburgh

Publications: 54

Stephen Gilmore

Stephen Gilmore

University of Edinburgh

Publications: 46

Ufuk Topcu

Ufuk Topcu

The University of Texas at Austin

Publications: 46

Luca Cardelli

Luca Cardelli

University of Oxford

Publications: 40

Bernd Becker

Bernd Becker

University of Freiburg

Publications: 40

Michael Fisher

Michael Fisher

University of Manchester

Publications: 36

Thomas A. Henzinger

Thomas A. Henzinger

Institute of Science and Technology Austria

Publications: 35

Kim Guldstrand Larsen

Kim Guldstrand Larsen

Aalborg University

Publications: 35

Boudewijn R. Haverkort

Boudewijn R. Haverkort

Tilburg University

Publications: 35

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

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