What is he best known for?
The fields of study he is best known for:
- Geometry
- Algorithm
- Artificial intelligence
His primary areas of investigation include Algorithm, Surface reconstruction, Combinatorics, Surface and Voronoi diagram.
His Algorithm research is multidisciplinary, incorporating perspectives in Point cloud and Computer graphics.
His Surface reconstruction study integrates concerns from other disciplines, such as Bowyer–Watson algorithm, Mathematical optimization and Solid modeling.
His work on Simplicial homology and Simplicial complex as part of general Combinatorics study is frequently connected to Hilbert manifold and n-sphere, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Surface research integrates issues from Discrete mathematics, Point, Curvature and Algorithmics.
His Voronoi diagram research includes elements of Computational geometry, Geometric shape, Delaunay triangulation and Topology.
His most cited work include:
- Tight Cocone: A Water-tight Surface Reconstructor (257 citations)
- A simple algorithm for homeomorphic surface reconstruction (226 citations)
- A SIMPLE ALGORITHM FOR HOMEOMORPHIC SURFACE RECONSTRUCTION (207 citations)
What are the main themes of his work throughout his whole career to date?
Combinatorics, Algorithm, Discrete mathematics, Delaunay triangulation and Surface are his primary areas of study.
His Combinatorics research incorporates themes from Upper and lower bounds and Regular polygon.
As a part of the same scientific study, Tamal K. Dey usually deals with the Algorithm, concentrating on Surface reconstruction and frequently concerns with Geometric modeling.
His work focuses on many connections between Delaunay triangulation and other disciplines, such as Topology, that overlap with his field of interest in Geometric shape.
As part of the same scientific family, Tamal K. Dey usually focuses on Surface, concentrating on Voronoi diagram and intersecting with Medial axis and Point.
Tamal K. Dey has researched Ruppert's algorithm in several fields, including Polyhedron and Piecewise.
He most often published in these fields:
- Combinatorics (27.05%)
- Algorithm (22.95%)
- Discrete mathematics (17.47%)
What were the highlights of his more recent work (between 2017-2021)?
- Persistent homology (11.64%)
- Time complexity (8.22%)
- Homology (11.30%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Persistent homology, Time complexity, Homology, Combinatorics and Topological data analysis.
His research integrates issues of Filtration, Topological space, Pure mathematics, Zigzag and Sequence in his study of Persistent homology.
Filtration is frequently linked to Algorithm in his study.
His Time complexity research includes themes of Multiset, Computation and Bounded function.
His work on Dual graph is typically connected to Exponent as part of general Combinatorics study, connecting several disciplines of science.
His biological study spans a wide range of topics, including Artificial intelligence and Pattern recognition.
Between 2017 and 2021, his most popular works were:
- Computing Bottleneck Distance for 2-D Interval Decomposable Modules (18 citations)
- A Multifunctional Smart Textile Derived from Merino Wool/Nylon Polymer Nanocomposites as Next Generation Microwave Absorber and Soft Touch Sensor. (17 citations)
- Graph Reconstruction by Discrete Morse Theory (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Geometry
- Algorithm
Tamal K. Dey mainly investigates Algorithm, Persistent homology, Topological data analysis, Graph and Reconstruction algorithm.
Specifically, his work in Algorithm is concerned with the study of Computation.
Tamal K. Dey has included themes like Stability, Bounded function and Topological space in his Persistent homology study.
Tamal K. Dey combines subjects such as Discrete mathematics, Metric space, Matrix and Filtration with his study of Topological data analysis.
His study looks at the relationship between Graph and fields such as Spatial network, as well as how they intersect with chemical problems.
His research investigates the connection between Artificial intelligence and topics such as Simplicial complex that intersect with problems in Matrix multiplication.
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