What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Geometry
- Algorithm
Nina Amenta mainly investigates Voronoi diagram, Algorithm, Medial axis, Combinatorics and Local feature size.
Nina Amenta combines subjects such as Surface and Surface reconstruction with her study of Voronoi diagram.
Her Algorithm research is multidisciplinary, incorporating elements of Point cloud, Vector field and Rendering.
Her research integrates issues of Theoretical computer science, Skeletonization, Complement and Topological skeleton in her study of Medial axis.
Her Combinatorics research is multidisciplinary, relying on both Discrete mathematics, Planar, Smooth curves and Curve reconstruction.
Her studies in Bowyer–Watson algorithm integrate themes in fields like Pitteway triangulation and Constrained Delaunay triangulation.
Her most cited work include:
- A new Voronoi-based surface reconstruction algorithm (800 citations)
- The power crust (632 citations)
- Surface Reconstruction by Voronoi Filtering (586 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Combinatorics, Algorithm, Artificial intelligence, Geometry and Medial axis.
In general Combinatorics, her work in Pitteway triangulation and Hyperplane is often linked to Local feature size linking many areas of study.
Her work is dedicated to discovering how Algorithm, Point cloud are connected with Point set and other disciplines.
In her works, Nina Amenta undertakes multidisciplinary study on Medial axis and SIMPLE algorithm.
Her Delaunay triangulation research includes themes of Voronoi diagram and Regular polygon.
Her Voronoi diagram research includes elements of Surface, Surface reconstruction and Solid modeling.
She most often published in these fields:
- Combinatorics (21.70%)
- Algorithm (17.92%)
- Artificial intelligence (15.09%)
What were the highlights of her more recent work (between 2013-2021)?
- Geometry (13.21%)
- Embedding (4.72%)
- Parallel computing (6.60%)
In recent papers she was focusing on the following fields of study:
Nina Amenta spends much of her time researching Geometry, Embedding, Parallel computing, Visualization and Almost everywhere.
Her work carried out in the field of Geometry brings together such families of science as Lemma and Maxima and minima.
Her Parallel computing study combines topics in areas such as Sorting and Range query.
Visualization is a subfield of Artificial intelligence that Nina Amenta explores.
The study of Artificial intelligence is intertwined with the study of Theoretical computer science in a number of ways.
Her study looks at the relationship between Almost everywhere and topics such as Triangle mesh, which overlap with Translation, Space, Rigidity and Shape analysis.
Between 2013 and 2021, her most popular works were:
- 3D skeletons: a state-of-the-art report (120 citations)
- Helly's Theorem: New Variations and Applications (32 citations)
- Brute-Force k-Nearest Neighbors Search on the GPU (23 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Geometry
- Algorithm
Her primary areas of investigation include Parallel computing, CUDA, Sorting, Discrete mathematics and Computational geometry.
Her research in Parallel computing intersects with topics in Divide and conquer algorithms, Algorithm, Binary strings and Matching.
Her CUDA research incorporates elements of Range query and Instruction set.
Her work deals with themes such as General-purpose computing on graphics processing units and k-nearest neighbors algorithm, which intersect with Sorting.
Her research brings together the fields of Helly's theorem and Discrete mathematics.
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