What is he best known for?
The fields of study he is best known for:
- Geometry
- Artificial intelligence
- Mathematical analysis
His primary scientific interests are in Conformal map, Topology, Artificial intelligence, Surface and Computer vision.
Conformal map is a subfield of Mathematical analysis that Xianfeng Gu tackles.
His Mathematical analysis research incorporates themes from Surface, Curvature and Ricci flow.
He has researched Topology in several fields, including Singularity, Mesh parameterization, Differential form and Graphics.
Many of his studies on Artificial intelligence apply to Polygon mesh as well.
Xianfeng Gu interconnects Rectangle and Noise shaping in the investigation of issues within Surface.
His most cited work include:
- Geometry images (713 citations)
- Genus zero surface conformal mapping and its application to brain surface mapping (466 citations)
- Global conformal surface parameterization (376 citations)
What are the main themes of his work throughout his whole career to date?
Xianfeng Gu focuses on Conformal map, Topology, Surface, Artificial intelligence and Mathematical analysis.
His Conformal map study is focused on Geometry in general.
The various areas that Xianfeng Gu examines in his Topology study include Parametrization, Shape analysis and Texture mapping.
His Surface research also works with subjects such as
- Polygon mesh and related Computer graphics,
- Genus that intertwine with fields like Zero.
His work deals with themes such as Algorithm, Computer vision and Pattern recognition, which intersect with Artificial intelligence.
He has included themes like Surface, Yamabe flow, Ricci flow and Gaussian curvature in his Mathematical analysis study.
He most often published in these fields:
- Conformal map (37.93%)
- Topology (30.34%)
- Surface (27.24%)
What were the highlights of his more recent work (between 2016-2021)?
- Conformal map (37.93%)
- Algorithm (16.21%)
- Surface (27.24%)
In recent papers he was focusing on the following fields of study:
His main research concerns Conformal map, Algorithm, Surface, Artificial intelligence and Polygon mesh.
The concepts of his Conformal map study are interwoven with issues in Distortion, Texture mapping, Graphics, Computation and Ricci flow.
His studies deal with areas such as Quadrilateral, Divisor and Holomorphic function, Parallelizable manifold, Topology as well as Surface.
His study ties his expertise on Planar together with the subject of Topology.
His research in Artificial intelligence intersects with topics in Point, Computer vision and Pattern recognition.
His Polygon mesh research is multidisciplinary, incorporating elements of Ruppert's algorithm, Computer graphics, Curvature and Parameterized complexity.
Between 2016 and 2021, his most popular works were:
- Spherical Parameterization Balancing Angle and Area Distortions (21 citations)
- Wireless Sensor Networks (20 citations)
- A Geometric Understanding of Deep Learning (19 citations)
In his most recent research, the most cited papers focused on:
- Geometry
- Artificial intelligence
- Mathematical analysis
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Ricci flow, Mode and Autoencoder.
His biological study spans a wide range of topics, including Point, Diffeomorphism and Computer vision.
In his work, Surface is strongly intertwined with Complex geometry, which is a subfield of Computer vision.
In his research on the topic of Algorithm, Robustness, Polycube, Surface and Polygon mesh is strongly related with Computer graphics.
His work deals with themes such as Conformal map, Computational geometry and Metric, which intersect with Ricci flow.
The Conformal map study combines topics in areas such as Mesh generation and Mesh parameterization.
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