What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Geometry
His scientific interests lie mostly in Algorithm, Polygon mesh, Computer vision, Artificial intelligence and Geometry.
Chandrajit L. Bajaj has researched Algorithm in several fields, including Computer Aided Design, CAD, Voronoi diagram, Surface and Visualization.
Chandrajit L. Bajaj has included themes like Smoothing, Hexahedron, Mesh generation, Volume mesh and Propagation of uncertainty in his Polygon mesh study.
His study in Computer vision is interdisciplinary in nature, drawing from both Computer graphics and Adaptive method.
His work on Image contrast, 3D reconstruction and Geometric primitive as part of his general Artificial intelligence study is frequently connected to Low-pass filter, thereby bridging the divide between different branches of science.
As a member of one scientific family, Chandrajit L. Bajaj mostly works in the field of Geometry, focusing on Mathematical analysis and, on occasion, Algebraic curve.
His most cited work include:
- Contour trees and small seed sets for isosurface traversal (292 citations)
- Patient-Specific Vascular NURBS Modeling for Isogeometric Analysis of Blood Flow (286 citations)
- Automatic reconstruction of surfaces and scalar fields from 3D scans (285 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Algorithm, Artificial intelligence, Visualization, Computer vision and Surface.
His Algorithm research is multidisciplinary, incorporating perspectives in Voronoi diagram, Polygon mesh and Theoretical computer science.
He combines subjects such as Quadrilateral, Mesh generation, Volume mesh and Hexahedron with his study of Polygon mesh.
His studies in Visualization integrate themes in fields like Scalability and Computer graphics.
His Surface study incorporates themes from Algebraic surface and Polynomial, Mathematical analysis.
His Mathematical analysis research focuses on Finite element method and how it relates to Applied mathematics.
He most often published in these fields:
- Algorithm (17.39%)
- Artificial intelligence (16.11%)
- Visualization (12.79%)
What were the highlights of his more recent work (between 2013-2021)?
- Algorithm (17.39%)
- Artificial intelligence (16.11%)
- Pattern recognition (4.35%)
In recent papers he was focusing on the following fields of study:
Chandrajit L. Bajaj focuses on Algorithm, Artificial intelligence, Pattern recognition, Polygon mesh and Voronoi diagram.
His Algorithm research integrates issues from Grid, Row and Rank.
His studies link Computer vision with Artificial intelligence.
His studies deal with areas such as Rate of convergence, Quadratic equation, Vertex and Topology as well as Polygon mesh.
The study incorporates disciplines such as Clipping, Delaunay triangulation, Surface and Volume mesh in addition to Voronoi diagram.
His work on Surface reconstruction as part of general Surface research is often related to Convexity, thus linking different fields of science.
Between 2013 and 2021, his most popular works were:
- QUADRATIC SERENDIPITY FINITE ELEMENTS ON POLYGONS USING GENERALIZED BARYCENTRIC COORDINATES. (59 citations)
- Computational reconstitution of spine calcium transients from individual proteins (41 citations)
- CONSTRUCTION OF SCALAR AND VECTOR FINITE ELEMENT FAMILIES ON POLYGONAL AND POLYHEDRAL MESHES. (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Operating system
Chandrajit L. Bajaj mostly deals with Algorithm, Artificial intelligence, Mathematical optimization, Regular polygon and Pattern recognition.
Chandrajit L. Bajaj interconnects Approximate inference, Geometric shape, Tensor, Representation and Synchronization in the investigation of issues within Algorithm.
His Artificial intelligence research incorporates themes from Neurophysiology and Dynamical simulation.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Proof of concept and Robustness.
Within one scientific family, he focuses on topics pertaining to Quadrilateral under Regular polygon, and may sometimes address concerns connected to Pure mathematics and Basis function.
His Pure mathematics study combines topics in areas such as Mixed finite element method, Polygon mesh, Mesh generation, Vertex and Extended finite element method.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
