What is he best known for?
The fields of study he is best known for:
- Geometry
- Artificial intelligence
- Topology
His scientific interests lie mostly in Delaunay triangulation, Voronoi diagram, Combinatorics, Bowyer–Watson algorithm and Constrained Delaunay triangulation.
His Delaunay triangulation research incorporates themes from Polyhedron, Surface and Boundary.
His research in Surface intersects with topics in Function and Interpolation, Mathematical analysis, Hausdorff distance.
His Combinatorics research is multidisciplinary, incorporating elements of Power diagram, Centroidal Voronoi tessellation, Weighted Voronoi diagram and Computational geometry.
His work on Surface triangulation as part of his general Constrained Delaunay triangulation study is frequently connected to Triangulation, thereby bridging the divide between different branches of science.
As a member of one scientific family, he mostly works in the field of Ruppert's algorithm, focusing on Hausdorff dimension and, on occasion, Line segment.
His most cited work include:
- Geometric structures for three-dimensional shape representation (522 citations)
- Shape reconstruction from planar cross sections (429 citations)
- Algorithmic Geometry (318 citations)
What are the main themes of his work throughout his whole career to date?
Jean-Daniel Boissonnat mostly deals with Combinatorics, Delaunay triangulation, Voronoi diagram, Topology and Bowyer–Watson algorithm.
His Combinatorics study incorporates themes from Discrete mathematics, Computational geometry, Point and Convex hull.
Jean-Daniel Boissonnat interconnects Manifold and Euclidean space in the investigation of issues within Delaunay triangulation.
In general Voronoi diagram, his work in Power diagram is often linked to Diagram linking many areas of study.
In his work, Interpolation is strongly intertwined with Surface, which is a subfield of Topology.
His Constrained Delaunay triangulation research includes elements of Computer vision and Artificial intelligence.
He most often published in these fields:
- Combinatorics (46.79%)
- Delaunay triangulation (31.19%)
- Voronoi diagram (21.56%)
What were the highlights of his more recent work (between 2016-2021)?
- Combinatorics (46.79%)
- Persistent homology (7.80%)
- Delaunay triangulation (31.19%)
In recent papers he was focusing on the following fields of study:
Combinatorics, Persistent homology, Delaunay triangulation, Simplicial complex and Manifold are his primary areas of study.
His Combinatorics research incorporates elements of Dimension and Regular polygon.
His Persistent homology study is associated with Topology.
His studies deal with areas such as Structure, Voronoi diagram, Probabilistic logic and Metric as well as Delaunay triangulation.
Jean-Daniel Boissonnat is interested in Bowyer–Watson algorithm, which is a branch of Voronoi diagram.
His Manifold research includes themes of Computational geometry, Triangulation and Piecewise linear manifold.
Between 2016 and 2021, his most popular works were:
- Geometric and Topological Inference (35 citations)
- Delaunay triangulation of manifolds (22 citations)
- The reach, metric distortion, geodesic convexity and the variation of tangent spaces. (14 citations)
In his most recent research, the most cited papers focused on:
- Geometry
- Artificial intelligence
- Topology
Jean-Daniel Boissonnat spends much of his time researching Combinatorics, Euclidean space, Manifold, Simplicial complex and Persistent homology.
Jean-Daniel Boissonnat has researched Combinatorics in several fields, including Tree, Tangent space and Data structure.
His work carried out in the field of Euclidean space brings together such families of science as Delaunay triangulation, Shortest path problem, Regular polygon, Voronoi diagram and Ball.
His research links Algorithm with Voronoi diagram.
His studies in Simplicial complex integrate themes in fields like Collapse, Sequence, Set and Simplex.
His research in Persistent homology focuses on subjects like Topological data analysis, which are connected to Nonlinear dimensionality reduction, External Data Representation, Inference, Topology and Computational geometry.
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