What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Mathematical analysis
- Artificial intelligence
His primary scientific interests are in Mathematical analysis, Convolution, Algorithm, Vibration and Applied mathematics.
His study involves Finite difference, Singularity, Numerical analysis and Subspace topology, a branch of Mathematical analysis.
The study incorporates disciplines such as Heteroclinic orbit, Field, Beam, Singular solution and Bistability in addition to Convolution.
Guo-Wei Wei has included themes like Multiresolution analysis, Partial differential equation, Numerical stability, Mathematical optimization and Wavelet in his Algorithm study.
The various areas that Guo-Wei Wei examines in his Vibration study include Geometry, Boundary value problem and Robustness.
His Applied mathematics study combines topics from a wide range of disciplines, such as Computer simulation, Benchmark, Nonlinear system, Eigenvalues and eigenvectors and Dirichlet distribution.
His most cited work include:
- Improvements to the APBS biomolecular solvation software suite. (406 citations)
- Methods for performing DAF data filtering and padding (286 citations)
- High order matched interface and boundary method for elliptic equations with discontinuous coefficients and singular sources (268 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical analysis, Statistical physics, Algorithm, Persistent homology and Convolution.
The concepts of his Mathematical analysis study are interwoven with issues in Geometry and Wavelet.
His work carried out in the field of Statistical physics brings together such families of science as Molecular dynamics, Solvation, Poisson–Boltzmann equation, Variational principle and Differential geometry.
His Algorithm study frequently draws connections to adjacent fields such as Partial differential equation.
In his research on the topic of Persistent homology, Graph theory is strongly related with Artificial intelligence.
Guo-Wei Wei combines subjects such as Spectral method and Applied mathematics with his study of Convolution.
He most often published in these fields:
- Mathematical analysis (24.92%)
- Statistical physics (15.58%)
- Algorithm (15.26%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (10.59%)
- Severe acute respiratory syndrome coronavirus 2 (5.92%)
- Machine learning (5.30%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Severe acute respiratory syndrome coronavirus 2, Machine learning, Persistent homology and Genome.
His study focuses on the intersection of Artificial intelligence and fields such as Graph theory with connections in the field of Structure and Virtual screening.
His Persistent homology research is included under the broader classification of Topology.
His study in Topology is interdisciplinary in nature, drawing from both Graph and Representation.
His Deep learning study integrates concerns from other disciplines, such as Artificial neural network and Algorithm.
His work in Algorithm is not limited to one particular discipline; it also encompasses Differential geometry.
Between 2018 and 2021, his most popular works were:
- Mutations Strengthened SARS-CoV-2 Infectivity. (87 citations)
- Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges (54 citations)
- AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening. (39 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Mathematical analysis
- Artificial intelligence
Guo-Wei Wei mostly deals with Artificial intelligence, Severe acute respiratory syndrome coronavirus 2, Genotyping, Graph theory and Deep learning.
His Artificial intelligence research incorporates elements of Topology and Machine learning.
His work deals with themes such as Virtual screening and Differential geometry, which intersect with Graph theory.
His Deep learning research is multidisciplinary, incorporating perspectives in Algorithm, Persistent homology, Convolutional neural network and Algebraic topology.
In his papers, Guo-Wei Wei integrates diverse fields, such as Algorithm and Estimator.
His Persistent homology research includes themes of Eigenvalues and eigenvectors, Statistical physics, Graph and Laplace operator.
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