Yousef Saad

About

Yousef Saad is a Professor in the Department of Computer Science & Engineering at the University of Minnesota, where he has been a faculty member since 1990. He holds the title of CSE Distinguished Professor and the William Norris Land Grant Chair in Large-Scale Computing. Saad's research focuses on numerical linear algebra, sparse matrix computations, iterative methods for linear systems and eigenvalue problems, parallel algorithms in numerical linear algebra, and matrix methods for machine learning. His work has contributed significantly to the development of algorithms and methods in these areas, with recent research including parallel algebraic recursive multilevel solvers and multilevel graph-based methods for data exploration. Saad has a distinguished academic background with two Ph.D. degrees from the University of Grenoble, France, and a B.S. in Mathematics from the University of Algiers. Prior to his current position, he served as a senior computer scientist and associate professor at the University of Illinois at Urbana-Champaign and as a senior scientist at the Research Institute for Advanced Computer Science. Saad has received numerous awards, including the 2023 SIAM John von Neumann Prize, and has been recognized as a Fellow of the AAAS and SIAM. His contributions to the field are reflected in his extensive publication record and his leadership in advancing large-scale computing and numerical methods.

Research topics

• Mathematical analysis
• Artificial Intelligence
• Mathematics
• Computer Science
• Applied mathematics
• Geometry
• Classical mechanics
• Theoretical computer science
• Mathematical optimization
• Algorithm

Selected publications

• Graph coarsening: from scientific computing to machine learning

  SeMA Journal · 2022 · 33 citations

  Abstract The general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning. The goal of this paper is to take a broad look into coarsening techniques that have been successfully deployed in scientific computing and see how similar principles are finding their way in more recent applications related to machine learning. In scientific computing, coarsening plays…

  DOI

• Shanks and Anderson-type acceleration techniques for systems of nonlinear equations

  IMA Journal of Numerical Analysis · 2021 · 12 citations

  Senior authorCorresponding

  Abstract This paper examines a number of extrapolation and acceleration methods and introduces a few modifications of the standard Shanks transformation that deal with general sequences. One of the goals of the paper is to lay out a general framework that encompasses most of the known acceleration strategies. The paper also considers the Anderson Acceleration (AA) method under a new light and exploits a connection with quasi-Newton methods in order to establish local linear convergence results o…

  DOI

• parGeMSLR: A parallel multilevel Schur complement low-rank preconditioning and solution package for general sparse matrices

  Parallel Computing · 2022-07-25 · 7 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• The Origin and Development of Krylov Subspace Methods

  Computing in Science & Engineering · 2022-07-01 · 5 citations

  article1st authorCorresponding

  Krylov subspace methods have had unparalleled success in solving real-life problems across disciplines ranging from computational fluid dynamics to statistics, machine learning, control theory, and computational chemistry, among many others. This article provides a brief history of these methods, discussing their origin, their expansion, and the lives of the people behind them.

  PublisherDOI

• Acceleration methods for fixed-point iterations

  Acta Numerica · 2025-07-01 · 3 citations

  articleOpen access1st authorCorresponding

  A pervasive approach in scientific computing is to express the solution to a given problem as the limit of a sequence of vectors or other mathematical objects. In many situations these sequences are generated by slowly converging iterative procedures, and this led practitioners to seek faster alternatives to reach the limit. ‘Acceleration techniques’ comprise a broad array of methods specifically designed with this goal in mind. They started as a means of improving the convergence of general sca…

  PublisherOA PDFDOI

Recent grants

• Numerical Linear Algebra and Approximation Theory Methods for Efficient Data Exploration

  NSF · $272k · 2005–2009

• Advances in Robust Multilevel Preconditioning Methods for Sparse Linear Systems

  NSF · $300k · 2019–2023

• CDI Type I: Collaborative research: Materials Informatics: Computational tools for discovery and design

  NSF · $346k · 2009–2013

Frequent coauthors

• James R. Chelikowsky

  The University of Texas at Austin

  50 shared

• Yuanzhe Xi

  Emory University

  39 shared

• Ruipeng Li

  27 shared

• Shashanka Ubaru

  25 shared

• Efstratios Gallopoulos

  University of Patras

  21 shared

• Kesheng Wu

  20 shared

• Pascal Hénon

  19 shared

• Martin H. Schultz

  17 shared

Labs

• Department of Computer Science & EngineeringPI

Awards & honors

• SIAM John von Neumann Prize (2023)
• William Norris Land Grant Chair in Large-Scale Computing (20…
• American Association for the Advancement of Science (AAAS) F…
• SIAM Fellows Program (2010)
• CSE Distinguished Professor (2005)

Similar researchers at University of Minnesota

• Dan Knightsh-97
• Joseph Konstanh-74
• Chad L. Myersh-72