H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Mathematics H-index 54 Citations 20,173 106 World Ranking 404 National Ranking 213
Computer Science H-index 69 Citations 68,472 154 World Ranking 889 National Ranking 536

Research.com Recognitions

Awards & Achievements

2010 - IEEE John von Neumann Medal “For laying the foundations for the fields of automata and language theory and many seminal contributions to theoretical computer science.”

2009 - Member of the National Academy of Sciences

2009 - SIAM Fellow For advances in the design and analysis of algorithms.

2008 - ACM Karl V. Karlstrom Outstanding Educator Award For his vision of and impact on computer science, including co- authoring field-defining texts on theory and algorithms, which continue to influence students 40 years later, advising PhD students who themselves are now contributing greatly to computer science, and providing influential leadership in computer science research and education at the national and international level.

1994 - ACM Fellow For fundamental achievements in the design and analysis of algorithms and data structures.

1989 - Member of the National Academy of Engineering For fundamental contributions to computer algorithms and for authorship of outstanding computer science textbooks.

1987 - Fellow of the American Academy of Arts and Sciences

1987 - Fellow of the American Association for the Advancement of Science (AAAS)

1987 - Fellow of the American Association for the Advancement of Science (AAAS)

1987 - IEEE Fellow For contributions to the field of computing.

1986 - A. M. Turing Award For fundamental achievements in the design and analysis of algorithms and data structures.

Foreign Member, Chinese Academy of Sciences

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Algorithm
  • Programming language

John E. Hopcroft focuses on Combinatorics, Discrete mathematics, Theoretical computer science, Algorithm and Automata theory. His research investigates the connection with Combinatorics and areas like Decidability which intersect with concerns in Reachability, Set theory, Petri net and Mathematical logic. As a part of the same scientific study, he usually deals with the Discrete mathematics, concentrating on Set and frequently concerns with Tracking, Structure, The Internet, Data set and Dimension.

His Theoretical computer science research is multidisciplinary, incorporating elements of Sorting, Model of computation, Data structure and Analysis of algorithms. John E. Hopcroft combines subjects such as Graph isomorphism, Queue, Function and Constant with his study of Algorithm. His work carried out in the field of Automata theory brings together such families of science as Computability, Theory of computation and Range.

His most cited work include:

  • Introduction to Automata Theory, Languages, and Computation (10835 citations)
  • The Design and Analysis of Computer Algorithms (7880 citations)
  • An $n^{5/2} $ Algorithm for Maximum Matchings in Bipartite Graphs (2331 citations)

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

John E. Hopcroft mainly investigates Artificial intelligence, Discrete mathematics, Combinatorics, Theoretical computer science and Algorithm. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Community structure and Pattern recognition. His work investigates the relationship between Machine learning and topics such as Structure that intersect with problems in Data science.

The study incorporates disciplines such as Nondeterministic finite automaton, Two-way deterministic finite automaton and Automaton in addition to Discrete mathematics. John E. Hopcroft is interested in Nested word, which is a branch of Theoretical computer science. John E. Hopcroft does research in Algorithm, focusing on Time complexity specifically.

He most often published in these fields:

  • Artificial intelligence (23.98%)
  • Discrete mathematics (23.98%)
  • Combinatorics (19.51%)

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

  • Artificial intelligence (23.98%)
  • Machine learning (10.16%)
  • Artificial neural network (5.69%)

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

Artificial intelligence, Machine learning, Artificial neural network, Pattern recognition and Representation are his primary areas of study. John E. Hopcroft usually deals with Artificial intelligence and limits it to topics linked to Network architecture and Maxima and minima and Randomness. John E. Hopcroft interconnects Test data, Structure, Community structure and Robustness in the investigation of issues within Machine learning.

John E. Hopcroft has included themes like Layer, Single image, Linear subspace and Residual in his Artificial neural network study. His work is dedicated to discovering how Representation, Spectral clustering are connected with Bounding overwatch, Heuristics and Theoretical computer science and other disciplines. Theoretical computer science and Algorithmics are commonly linked in his work.

Between 2014 and 2021, his most popular works were:

  • Stacked Generative Adversarial Networks (280 citations)
  • Snapshot Ensembles: Train 1, get M for free (223 citations)
  • Foundations of Data Science (120 citations)

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

  • Artificial intelligence
  • Algorithm
  • Programming language

His primary areas of study are Artificial intelligence, Machine learning, Artificial neural network, Representation and Pattern recognition. His Artificial intelligence study incorporates themes from Manifold, Structure and Linear subspace. His Machine learning study combines topics from a wide range of disciplines, such as Network architecture, Single node, Community structure and Robustness.

In general Artificial neural network study, his work on Supervised learning often relates to the realm of Gaussian, thereby connecting several areas of interest. In his research on the topic of Pattern recognition, Conditional entropy, Entropy, Entropy and Discriminative model is strongly related with Generative model. His study in Spectral clustering is interdisciplinary in nature, drawing from both Closeness, Theoretical computer science and Bounding overwatch.

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

Introduction to Automata Theory, Languages, and Computation

John E. Hopcroft;Rajeev Motwani;Rotwani;Jeffrey D. Ullman.
(1979)

20776 Citations

The Design and Analysis of Computer Algorithms

Alfred V. Aho;John E. Hopcroft.
(1974)

14310 Citations

Data structures and algorithms

Alfred V. Aho;John E. Hopcroft;Jeffrey Ullman.
(1983)

4875 Citations

An $n^{5/2} $ Algorithm for Maximum Matchings in Bipartite Graphs

John E. Hopcroft;Richard M. Karp.
SIAM Journal on Computing (1973)

3519 Citations

Formal Languages and Their Relation to Automata

John E. Hopcroft;Jeffrey D. Ullman.
(1969)

2454 Citations

Efficient Planarity Testing

John Hopcroft;Robert Tarjan.
Journal of the ACM (1974)

1553 Citations

Introduction To Automata Theory, Languages And Computation, 3Rd Edition

John E. Hopcroft;Rajeev Motwani;Jeffrey D. Ullman.
(2012)

1291 Citations

An n log n algorithm for minimizing states in a finite automaton

John E. Hopcroft.
Theory of Machines and Computations#R##N#Proceedings of an International Symposium on the Theory of Machines and Computations Held at Technion in Haifa, Israel, on August 16–19, 1971 (1971)

1240 Citations

Algorithm 447: efficient algorithms for graph manipulation

John Hopcroft;Robert Tarjan.
Communications of The ACM (1973)

1202 Citations

Dividing a Graph into Triconnected Components

John E. Hopcroft;Robert Endre Tarjan.
SIAM Journal on Computing (1973)

1034 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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