Laura Grigori

photo placeholder image

laura.grigori@epfl.ch +41 21 693 19 78 https://www.epfl.ch/labs/hpnalgs/

EPFL SB MATH HPNALGS
MA C2 652 (Bâtiment MA)
Station 8
1015 Lausanne

+41 21 693 19 78
Office:  MA C2 652
EPFL > SB > MATH > HPNALGS

Web site:  Web site:  https://www.epfl.ch/labs/hpnalgs/

EPFL SB MATH HPNALGS
MA C2 652 (Bâtiment MA)
Station 8
1015 Lausanne

+41 21 693 19 78
Office:  MA C2 652
EPFL > SB > SB-SMA > SMA-ENS

Web site:  Web site:  https://sma.epfl.ch/

vCard
Administrative data

Fields of expertise

Numerical linear algebra, deterministic and randomized algorithms 
High performance computing for scientific applications 
High dimensional problems and tensor computations 
Applications in astrophysics, molecular simulations, etc
Combinatorial scientific computing 
Mathematical Software 

Awards

SIAM Supercomputing Career Prize

In acknowledgement of her outstanding contributions to scientific computing, particularly communication-avoiding algorithms

2024

SIAM Fellow

For contributions to numerical linear algebra, including communication-avoiding algorithms.

2020

ERC Synergy Grant

Extreme-scale Mathematically-based Computational Chemistry

2019

SIAM SIAG on Supercomputing Best Paper Prize

With J. Demmel, M. Hoemmen, and J. Langou for the paper Communication-Optimal Parallel and Sequential QR and LU Factorizations, SISC 2012

2016

Teaching & PhD

Teaching

Mathematics

PhD Students

Fakih Israa, Martinez Aguilar Mariana Graciela, Xue Zhipeng,

Courses

Numerical analysis and computational mathematics

The course provides an introduction to scientific computing. Several numerical methods are presented for the computer solution of mathematical problems arising in different applications. The software MATLAB is used to solve the problems and verify the theoretical properties of the numerical methods.

HPC for numerical methods and data analysis

  • Introduction to high performance computing
  • Overview of state-of-the-art parallel architectures and MPI programming technique
  • Factorization methods and communication avoiding algorithms
  • Randomization for solving large scale problems
  • Low rank matrix approximation algorithms, deterministic and randomized approaches
  • Krylov subspace iterative solve