What is he best known for?
The fields of study he is best known for:
- Operating system
- Algorithm
- Artificial intelligence
Michael A. Bender mostly deals with Data structure, Combinatorics, Algorithm, Cache-oblivious algorithm and Parallel computing.
Michael A. Bender combines subjects such as Theoretical computer science, Quotient and Bloom filter with his study of Data structure.
His work investigates the relationship between Combinatorics and topics such as Upper and lower bounds that intersect with problems in Vertex, Approximation algorithm and Job shop scheduling.
His research investigates the connection between Algorithm and topics such as Robot that intersect with issues in Intelligent control and Counterexample.
His studies in Cache-oblivious algorithm integrate themes in fields like Binary logarithm, Memory hierarchy and Hierarchy.
His study in Parallel computing is interdisciplinary in nature, drawing from both Distributed computing, STREAMS, Concurrent data structure, Structure and Record locking.
His most cited work include:
- The LCA Problem Revisited (685 citations)
- The power of a pebble: exploring and mapping directed graphs (230 citations)
- Flow and stretch metrics for scheduling continuous job streams (224 citations)
What are the main themes of his work throughout his whole career to date?
Michael A. Bender mainly investigates Algorithm, Combinatorics, Data structure, Parallel computing and Approximation algorithm.
His study looks at the relationship between Algorithm and fields such as Robot, as well as how they intersect with chemical problems.
His work carried out in the field of Combinatorics brings together such families of science as Discrete mathematics and Upper and lower bounds.
His Data structure research includes elements of Theoretical computer science, Cache-oblivious algorithm and Bloom filter.
His biological study spans a wide range of topics, including Block, Memory hierarchy and Hierarchy.
His Approximation algorithm study integrates concerns from other disciplines, such as Time complexity, Optimization problem, Scheduling and Travelling salesman problem.
He most often published in these fields:
- Algorithm (24.23%)
- Combinatorics (22.91%)
- Data structure (19.38%)
What were the highlights of his more recent work (between 2016-2021)?
- Data structure (19.38%)
- Algorithm (24.23%)
- Parallel computing (14.54%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Data structure, Algorithm, Parallel computing, Upper and lower bounds and Discrete mathematics.
He has included themes like Data mining, Out-of-core algorithm, Speedup and Bloom filter in his Data structure study.
His Algorithm study combines topics from a wide range of disciplines, such as Smoothing, Sequence, Cache-oblivious algorithm and De Bruijn graph.
His Parallel computing research incorporates themes from Scheduling, Algorithmics and Orders of magnitude.
His research integrates issues of Auxiliary memory and Subadditivity in his study of Upper and lower bounds.
His Space research focuses on Range and how it connects with Combinatorics.
Between 2016 and 2021, his most popular works were:
- A General-Purpose Counting Filter: Making Every Bit Count (68 citations)
- Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index. (51 citations)
- Squeakr: an exact and approximate k-mer counting system (36 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Algorithm
- Artificial intelligence
Michael A. Bender mainly focuses on Data structure, De Bruijn graph, Bloom filter, Algorithm and Quotient filter.
His Data structure research incorporates elements of Theoretical computer science, Usability, Out-of-core algorithm, Arithmetic and Exploit.
His Bloom filter study incorporates themes from Tree, Sequence and Quotient.
His Sequence research includes themes of Upper and lower bounds, False positive paradox, Probabilistic logic and Index.
His study in the fields of Tree traversal under the domain of Algorithm overlaps with other disciplines such as k-mer.
His work deals with themes such as Data mining, Data set, Speedup and Locality of reference, which intersect with Quotient filter.
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