What is he best known for?
The fields of study he is best known for:
- Combinatorics
- Algebra
- Discrete mathematics
His primary areas of study are Combinatorics, Discrete mathematics, Random matrix, Random graph and Eigenvalues and eigenvectors.
His Combinatorics research includes themes of Matrix, Upper and lower bounds, Random variable and Singularity.
As a member of one scientific family, Van Vu mostly works in the field of Discrete mathematics, focusing on Random element and, on occasion, Invertible matrix and Fair coin.
Van Vu brings together Random matrix and Universality to produce work in his papers.
He interconnects Random regular graph, Degree, Areas of mathematics, Combinatorial number theory and Lipschitz continuity in the investigation of issues within Random graph.
Van Vu focuses mostly in the field of Eigenvalues and eigenvectors, narrowing it down to topics relating to Power law and, in certain cases, Adjacency matrix.
His most cited work include:
- Spectra of random graphs with given expected degrees (400 citations)
- Random matrices: Universality of local eigenvalue statistics (370 citations)
- Random matrices: Universality of ESDs and the circular law (310 citations)
What are the main themes of his work throughout his whole career to date?
Van Vu focuses on Combinatorics, Discrete mathematics, Random matrix, Random variable and Eigenvalues and eigenvectors.
Van Vu has included themes like Matrix and Upper and lower bounds in his Combinatorics study.
Polytope is closely connected to Central limit theorem in his research, which is encompassed under the umbrella topic of Discrete mathematics.
His studies deal with areas such as Singular value, Hermitian matrix and Zero as well as Random matrix.
The Random variable study which covers Inverse that intersects with Littlewood–Offord problem.
His work deals with themes such as Randomness, Symmetric matrix, Spectrum and Mathematical physics, which intersect with Eigenvalues and eigenvectors.
He most often published in these fields:
- Combinatorics (69.77%)
- Discrete mathematics (44.65%)
- Random matrix (28.84%)
What were the highlights of his more recent work (between 2015-2021)?
- Combinatorics (69.77%)
- Random matrix (28.84%)
- Discrete mathematics (44.65%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Combinatorics, Random matrix, Discrete mathematics, Random variable and Pure mathematics.
The various areas that Van Vu examines in his Combinatorics study include Arbitrarily large, Bounded function and Torsion.
His Random matrix research includes elements of Singular value, Matrix and Multivariate random variable.
His study on Random graph is often connected to Circular law as part of broader study in Discrete mathematics.
His Random graph research focuses on Adjacency matrix and how it relates to Graph isomorphism problem and Random regular graph.
His Pure mathematics study combines topics from a wide range of disciplines, such as Random polynomials and Real roots.
Between 2015 and 2021, his most popular works were:
- Random Perturbation of Low Rank Matrices: Improving Classical Bounds (53 citations)
- Eigenvectors of random matrices (50 citations)
- On the number of real roots of random polynomials (41 citations)
In his most recent research, the most cited papers focused on:
- Combinatorics
- Algebra
- Mathematical analysis
Van Vu mainly focuses on Combinatorics, Random matrix, Discrete mathematics, Random polynomials and Pure mathematics.
In the subject of general Combinatorics, his work in Binary logarithm and Abelian group is often linked to Optimal density, thereby combining diverse domains of study.
Eigenvalues and eigenvectors covers Van Vu research in Random matrix.
Van Vu works in the field of Discrete mathematics, focusing on Random graph in particular.
His Random polynomials study also includes
- Real roots which connect with Complex number,
- Random graph theory that intertwine with fields like Random variable.
His Pure mathematics research is multidisciplinary, incorporating elements of Singular value and Matrix perturbation.
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