What is he best known for?
The fields of study he is best known for:
- Algorithm
- Programming language
- Time complexity
Dan Gusfield mainly investigates Algorithm, Combinatorics, Set, Theoretical computer science and Perfect phylogeny.
The study incorporates disciplines such as Graph and Generalized suffix tree, String, Suffix tree in addition to Algorithm.
The concepts of his Suffix tree study are interwoven with issues in Suffix array and Tandem repeat.
His research in Combinatorics focuses on subjects like Discrete mathematics, which are connected to Value and Fuzzy clustering.
In his research on the topic of Set, Analysis of algorithms is strongly related with Matching.
His Theoretical computer science research focuses on subjects like Phylogenetic tree, which are linked to Realization, Algorithmics and Field.
His most cited work include:
- Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology (3479 citations)
- Algorithms on strings, trees, and sequences (1836 citations)
- The Stable Marriage Problem: Structure and Algorithms (931 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Algorithm, Combinatorics, Discrete mathematics, Perfect phylogeny and Theoretical computer science.
His research integrates issues of String and Set in his study of Algorithm.
His work in the fields of Chordal graph, Graph and Perfect graph overlaps with other areas such as Data security.
His Perfect phylogeny study combines topics in areas such as Phylogenetic network, Phylogenetics, Missing data and Integer programming.
Dan Gusfield combines subjects such as Efficient algorithm and Data structure with his study of Theoretical computer science.
His Suffix tree research focuses on Tandem repeat and how it connects with Tree traversal.
He most often published in these fields:
- Algorithm (36.02%)
- Combinatorics (34.16%)
- Discrete mathematics (21.12%)
What were the highlights of his more recent work (between 2006-2020)?
- Algorithm (36.02%)
- Combinatorics (34.16%)
- Perfect phylogeny (18.63%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Algorithm, Combinatorics, Perfect phylogeny, Discrete mathematics and Integer programming.
His work in the fields of Algorithm, such as Dynamic programming, overlaps with other areas such as Folding.
His study in Combinatorics is interdisciplinary in nature, drawing from both Computational complexity theory, Phylogenetic network, Calculus and Computational problem.
His work carried out in the field of Perfect phylogeny brings together such families of science as Phylogenetics, Missing data and Extension.
In his research, Equivalence is intimately related to Generalization, which falls under the overarching field of Discrete mathematics.
His studies deal with areas such as Theoretical computer science, Systems biology, Range, Perl and Tree as well as Integer programming.
Between 2006 and 2020, his most popular works were:
- ReCombinatorics: The Algorithmics of Ancestral Recombination Graphs and Explicit Phylogenetic Networks (85 citations)
- A decomposition theory for phylogenetic networks and incompatible characters. (46 citations)
- Integer programming formulations and computations solving phylogenetic and population genetic problems with missing or genotypic data (37 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Programming language
- Time complexity
Dan Gusfield mainly focuses on Combinatorics, Perfect phylogeny, Discrete mathematics, Algorithm and Integer programming.
His studies in Combinatorics integrate themes in fields like Computational complexity theory, Phylogenetic network and Computational problem.
While the research belongs to areas of Computational complexity theory, Dan Gusfield spends his time largely on the problem of Analysis of algorithms, intersecting his research to questions surrounding Matching.
His work on Chordal graph and Equivalence as part of general Discrete mathematics study is frequently connected to If and only if, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His work in the fields of Dynamic programming overlaps with other areas such as Simple.
His Composition research incorporates themes from Theoretical computer science and Missing data.
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.
