What is he best known for?
The fields of study he is best known for:
- Algorithm
- Geometry
- Artificial intelligence
Jack Snoeyink mainly investigates Combinatorics, Algorithm, Discrete mathematics, Simple polygon and Regular polygon.
Many of his research projects under Combinatorics are closely connected to Protein family with Protein family, tying the diverse disciplines of science together.
His research integrates issues of Quadratic equation, Line, Geometry and SIMPLE algorithm in his study of Algorithm.
The study incorporates disciplines such as Pitteway triangulation and Minimum-weight triangulation in addition to Discrete mathematics.
His Regular polygon study incorporates themes from Path and Plane.
His study in the fields of Structure validation under the domain of Protein structure overlaps with other disciplines such as Component.
His most cited work include:
- MolProbity: all-atom contacts and structure validation for proteins and nucleic acids (3018 citations)
- MolProbity: More and better reference data for improved all-atom structure validation. (733 citations)
- Computing contour trees in all dimensions (369 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Combinatorics, Algorithm, Discrete mathematics, Voronoi diagram and Regular polygon.
The Combinatorics study combines topics in areas such as Plane, Line segment and Convex hull.
His work on Computational geometry as part of general Algorithm study is frequently linked to sort, therefore connecting diverse disciplines of science.
His Discrete mathematics research is multidisciplinary, incorporating perspectives in Delaunay triangulation, Monotone polygon and Degree.
Jack Snoeyink works mostly in the field of Voronoi diagram, limiting it down to concerns involving Pitteway triangulation and, occasionally, Minimum-weight triangulation.
His Regular polygon research is multidisciplinary, relying on both Upper and lower bounds, Polyhedron and Piecewise.
He most often published in these fields:
- Combinatorics (52.96%)
- Algorithm (19.86%)
- Discrete mathematics (18.82%)
What were the highlights of his more recent work (between 2009-2021)?
- Combinatorics (52.96%)
- Algorithm (19.86%)
- Regular polygon (10.80%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Combinatorics, Algorithm, Regular polygon, Simply connected space and Efficient algorithm.
His Combinatorics research is multidisciplinary, incorporating elements of Point and Euclidean distance.
His Algorithm study combines topics from a wide range of disciplines, such as Quadric, Intersection, Mathematical optimization and Range.
His Regular polygon research integrates issues from Time complexity, Polyhedron, Triangulation and Lebesgue integration.
Jack Snoeyink has researched Simply connected space in several fields, including Cover, Covering space, Equilateral triangle, Upper and lower bounds and Piecewise.
In his work, Line segment and Geometry is strongly intertwined with Simulation, which is a subfield of Efficient algorithm.
Between 2009 and 2021, his most popular works were:
- MolProbity: More and better reference data for improved all-atom structure validation. (733 citations)
- Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (156 citations)
- Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta (144 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Geometry
- Artificial intelligence
His primary scientific interests are in Combinatorics, Delaunay triangulation, Pitteway triangulation, Bowyer–Watson algorithm and Polygon.
His Combinatorics study typically links adjacent topics like Euclidean distance.
His Delaunay triangulation study often links to related topics such as Discrete mathematics.
Jack Snoeyink combines subjects such as Minimum-weight triangulation and Constrained Delaunay triangulation with his study of Discrete mathematics.
His study in Bowyer–Watson algorithm is interdisciplinary in nature, drawing from both Power diagram, Centroidal Voronoi tessellation and Weighted Voronoi diagram.
Artificial intelligence is closely connected to Computer vision in his research, which is encompassed under the umbrella topic of Polygon.
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