What is he best known for?
The fields of study he is best known for:
- Algorithm
- Combinatorics
- Artificial intelligence
His primary scientific interests are in Combinatorics, Discrete mathematics, Binary logarithm, Theoretical computer science and Algorithm.
His biological study spans a wide range of topics, including Computational complexity theory and Matching.
His study looks at the relationship between Discrete mathematics and fields such as Decoding methods, as well as how they intersect with chemical problems.
His Binary logarithm study integrates concerns from other disciplines, such as Connected component, Certificate and Parallel algorithm.
The study incorporates disciplines such as Scheduling, Job shop scheduling, Competitive analysis and Discrete geometry in addition to Theoretical computer science.
His Algorithm research is multidisciplinary, incorporating elements of Computational economics and Game theory.
His most cited work include:
- Hamsa: fast signature generation for zero-day polymorphic worms with provable attack resilience (280 citations)
- A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry. (211 citations)
- Complexities for Generalized Models of Self-Assembly (143 citations)
What are the main themes of his work throughout his whole career to date?
Ming-Yang Kao mostly deals with Combinatorics, Discrete mathematics, Time complexity, Algorithm and Mathematical optimization.
His work is connected to Binary logarithm, Planar graph, Bipartite graph, Approximation algorithm and Vertex, as a part of Combinatorics.
His Discrete mathematics research incorporates themes from Graph, Speedup and Adjacency list.
His study in Time complexity is interdisciplinary in nature, drawing from both Huffman coding, Graph theory and Hamming distance.
In his study, Computational problem is inextricably linked to Theoretical computer science, which falls within the broad field of Algorithm.
The concepts of his Mathematical optimization study are interwoven with issues in Investment strategy, Trading strategy and Portfolio.
He most often published in these fields:
- Combinatorics (48.39%)
- Discrete mathematics (29.49%)
- Time complexity (26.73%)
What were the highlights of his more recent work (between 2006-2017)?
- Algorithm (23.50%)
- Combinatorics (48.39%)
- Time complexity (26.73%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Algorithm, Combinatorics, Time complexity, Discrete mathematics and Mathematical optimization.
In general Algorithm, his work in Approximation algorithm and Randomized algorithm is often linked to Extrapolation linking many areas of study.
His Combinatorics research is multidisciplinary, relying on both Sequence and Statistical model.
His Statistical model research is multidisciplinary, incorporating perspectives in Binary logarithm and Pattern recognition.
As a part of the same scientific study, he usually deals with the Time complexity, concentrating on Hamming distance and frequently concerns with Coding theory, Enumeration, Linked list and Word.
His study brings together the fields of Upper and lower bounds and Discrete mathematics.
Between 2006 and 2017, his most popular works were:
- Encyclopedia of Algorithms (102 citations)
- Reversible sketches: enabling monitoring and analysis over high-speed data streams (97 citations)
- Randomized Self-assembly for Approximate Shapes (67 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Combinatorics
- Artificial intelligence
Ming-Yang Kao focuses on Algorithm, Discrete mathematics, Combinatorics, Time complexity and Approximation algorithm.
His work on Computational geometry as part of general Algorithm study is frequently linked to Encyclopedia, bridging the gap between disciplines.
His Discrete mathematics study integrates concerns from other disciplines, such as Upper and lower bounds and Theory of computation.
His research combines Bounded function and Combinatorics.
The Time complexity study combines topics in areas such as Efficient algorithm and Reconstruction method.
His Approximation algorithm research incorporates elements of Travelling salesman problem, Bottleneck traveling salesman problem, Similarity measure, Point and Similitude.
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