D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Mathematics D-index 32 Citations 9,949 86 World Ranking 1744 National Ranking 746

Research.com Recognitions

Awards & Achievements

2020 - ACM Fellow For contributions to algorithms and languages for numerical and scientific computing

2018 - IEEE Fellow For contributions to the development of technical-computing languages

2015 - Fellow of the American Mathematical Society For contributions to random matrix theory, numerical linear algebra, high-performance algorithms, and applications.

2011 - SIAM Fellow For contributions to mathematics and industry in the areas of numerical linear algebra, random matrix theory, and parallel computing.

1994 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Programming language
  • Mathematical analysis
  • Algebra

Alan Edelman mainly investigates Mathematical analysis, Programming language, Random matrix, Random element and Multivariate random variable. His Random matrix research incorporates themes from Wishart distribution, Nonnegative matrix, Pascal matrix and Pure mathematics. His studies deal with areas such as Random function and Sum of normally distributed random variables as well as Random element.

The various areas that Alan Edelman examines in his Algorithm study include Rayleigh quotient iteration, Divide-and-conquer eigenvalue algorithm, Newton's method and Sequential quadratic programming. His Polynomial matrix research includes themes of Eigenvalues and eigenvectors, Characteristic polynomial and Combinatorics. His Eigenvalues and eigenvectors research includes elements of Numerical linear algebra and Stiefel manifold.

His most cited work include:

  • The Geometry of Algorithms with Orthogonality Constraints (2156 citations)
  • Julia: A Fresh Approach to Numerical Computing (1600 citations)
  • Eigenvalues and condition numbers of random matrices (1206 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Random matrix, Eigenvalues and eigenvectors, Matrix, Combinatorics and Algorithm. His Random matrix study integrates concerns from other disciplines, such as Laguerre polynomials, Pure mathematics, Wishart distribution, Tridiagonal matrix and Applied mathematics. The concepts of his Eigenvalues and eigenvectors study are interwoven with issues in Singular value, Polynomial, Mathematical analysis, Condition number and Numerical linear algebra.

Matrix is a subfield of Algebra that Alan Edelman tackles. His Combinatorics course of study focuses on Discrete mathematics and Hadamard matrix. His research in Algorithm intersects with topics in Fourier transform, Mathematical optimization and Sample size determination.

He most often published in these fields:

  • Random matrix (24.86%)
  • Eigenvalues and eigenvectors (21.55%)
  • Matrix (18.23%)

What were the highlights of his more recent work (between 2013-2021)?

  • Random matrix (24.86%)
  • Matrix (18.23%)
  • Eigenvalues and eigenvectors (21.55%)

In recent papers he was focusing on the following fields of study:

Random matrix, Matrix, Eigenvalues and eigenvectors, Singular value and Programming language are his primary areas of study. His Random matrix study combines topics from a wide range of disciplines, such as Laguerre polynomials, Toeplitz matrix, Pure mathematics, Representation and Tridiagonal matrix. Alan Edelman interconnects Singular value decomposition, Quaternion, Hopf fibration and Square root in the investigation of issues within Matrix.

His studies deal with areas such as Mathematical analysis, Combinatorics, Wishart distribution, Condition number and Diagonal matrix as well as Eigenvalues and eigenvectors. His research integrates issues of Universality, Statistical physics, Kurtosis and Generalized singular value decomposition in his study of Singular value. His work on Compiler and Data type as part of general Programming language study is frequently linked to Multiple dispatch and Haskell, therefore connecting diverse disciplines of science.

Between 2013 and 2021, his most popular works were:

  • Julia: A Fresh Approach to Numerical Computing (1600 citations)
  • Julia: A Fresh Approach to Numerical Computing (116 citations)
  • Universal Differential Equations for Scientific Machine Learning (93 citations)

In his most recent research, the most cited papers focused on:

  • Programming language
  • Algebra
  • Mathematical analysis

His primary areas of investigation include Random matrix, Multiple dispatch, Eigenvalues and eigenvectors, Singular value and Numerical computing. His research investigates the connection between Random matrix and topics such as Matrix that intersect with issues in Hypergeometric function of a matrix argument, Constructive proof, Linear algebra and Square root. Multiple dispatch combines with fields such as Software deployment, Computation, Programmer, Dance and Bridging in his investigation.

His Eigenvalues and eigenvectors research is mostly focused on the topic Matrix analysis. Alan Edelman usually deals with Singular value and limits it to topics linked to Laguerre polynomials and Condition number, Central limit theorem, Hermite polynomials and Random variable. His Numerical computing investigation overlaps with other disciplines such as Algorithm and Theoretical computer science.

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.

Best Publications

The Geometry of Algorithms with Orthogonality Constraints

Alan Edelman;Tomás A. Arias;Steven T. Smith.
SIAM Journal on Matrix Analysis and Applications (1999)

2743 Citations

Julia: A Fresh Approach to Numerical Computing

Jeff Bezanson;Alan Edelman;Stefan Karpinski;Viral B. Shah.
Siam Review (2017)

2144 Citations

Eigenvalues and condition numbers of random matrices

Alan Edelman.
SIAM Journal on Matrix Analysis and Applications (1988)

1487 Citations

Matrix models for beta ensembles

Ioana Dumitriu;Alan Edelman.
Journal of Mathematical Physics (2002)

598 Citations

Julia: A Fast Dynamic Language for Technical Computing

Jeff Bezanson;Stefan Karpinski;Viral B. Shah;Alan Edelman.
arXiv: Programming Languages (2012)

596 Citations

How many zeros of a random polynomial are real

Alan Edelman;Eric Kostlan.
Bulletin of the American Mathematical Society (1995)

577 Citations

Random matrix theory

Alan Edelman;N. Raj Rao.
Acta Numerica (2005)

530 Citations

PetaBricks: a language and compiler for algorithmic choice

Jason Ansel;Cy Chan;Yee Lok Wong;Marek Olszewski.
programming language design and implementation (2009)

481 Citations

Modeling and rendering of weathered stone

Julie Dorsey;Alan Edelman;Henrik Wann Jensen;Justin Legakis.
international conference on computer graphics and interactive techniques (1999)

375 Citations

Polynomial roots from companion matrix eigenvalues

Alan Edelman;H. Murakami.
Mathematics of Computation (1995)

300 Citations

Editorial Boards

Journal of Cystic Fibrosis
(Impact Factor: 5.527)

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