Misha Kilmer

Misha Kilmer

Tufts University

H-index: 35

North America-United States

About Misha Kilmer

Misha Kilmer, With an exceptional h-index of 35 and a recent h-index of 23 (since 2020), a distinguished researcher at Tufts University, specializes in the field of numerical linear algebra, multilinear algebra, inverse problems, scientific computing.

His recent articles reflect a diverse array of research interests and contributions to the field:

Generating and managing deep tensor neural networks

Tensor Completion with BMD Factor Nuclear Norm Minimization

Subspace Recycling for Sequences of Shifted Systems with Applications in Image Recovery

Multigrid preconditioning for regularized least-squares problems

Tensor BM-Decomposition for Compression and Analysis of Spatio-Temporal Third-order Data

Recycling MMGKS for large-scale dynamic and streaming data

Tensor-based predictions from analysis of time-varying graphs

Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)

Misha Kilmer Information

University

Position

___

Citations(all)

8034

Citations(since 2020)

4147

Cited By

5357

hIndex(all)

35

hIndex(since 2020)

23

i10Index(all)

59

i10Index(since 2020)

41

Email

University Profile Page

Tufts University

Google Scholar

View Google Scholar Profile

Misha Kilmer Skills & Research Interests

numerical linear algebra

multilinear algebra

inverse problems

scientific computing

Top articles of Misha Kilmer

Title

Journal

Author(s)

Publication Date

Generating and managing deep tensor neural networks

2024/4/2

Tensor Completion with BMD Factor Nuclear Norm Minimization

arXiv preprint arXiv:2402.13068

Fan Tian

Mirjeta Pasha

Misha E Kilmer

Eric Miller

Abani Patra

2024/2/20

Subspace Recycling for Sequences of Shifted Systems with Applications in Image Recovery

arXiv preprint arXiv:2306.15049

Misha E Kilmer

Eric de Sturler

2023/6/26

Multigrid preconditioning for regularized least-squares problems

arXiv preprint arXiv:2306.11067

Matthias Bolten

Scott P MacLachlan

Misha E Kilmer

2023/6/19

Tensor BM-Decomposition for Compression and Analysis of Spatio-Temporal Third-order Data

arXiv preprint arXiv:2306.09201

Fan Tian

Misha E Kilmer

Eric Miller

Abani Patra

2023/6/15

Recycling MMGKS for large-scale dynamic and streaming data

arXiv preprint arXiv:2309.15759

Mirjeta Pasha

Eric de Sturler

Misha E Kilmer

2023/9/27

Tensor-based predictions from analysis of time-varying graphs

2022/7/12

Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR)

arXiv preprint arXiv:2204.09815

Ege Ozsar

Misha Kilmer

Eric Miller

Eric de Sturler

Arvind Saibaba

2022/4/21

Research Spotlights

SIAM Review

Misha E Kilmer

2022

Structured matrix approximations via tensor decompositions

SIAM Journal on Matrix Analysis and Applications

Misha E Kilmer

Arvind K Saibaba

2022/11/18

Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions

Advances in Computational Mathematics

Arvind K Saibaba

Rachel Minster

Misha E Kilmer

2022/10

Tensor-tensor algebra for optimal representation and compression of multiway data

Proceedings of the National Academy of Sciences

Misha E Kilmer

Lior Horesh

Haim Avron

Elizabeth Newman

2021/7/13

Mis-specified model supplementation

2021/1/5

Dynamic graph convolutional networks using the tensor M-product

Osman Asif Malik

Shashanka Ubaru

Lior Horesh

Misha E Kilmer

Haim Avron

2021

Multi-linear dynamical model reduction

2021/11/30

Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems

Journal of Computational Physics

Arvind K Saibaba

Pranjal Prasad

Eric De Sturler

Eric Miller

Misha E Kilmer

2021/9/1

Nonnegative tensor patch dictionary approaches for image compression and deblurring applications

SIAM Journal on Imaging Sciences

Elizabeth Newman

Misha E Kilmer

2020

Optimal multi-dimensional data compression by tensor-tensor decompositions tensor

2020/9/8

Randomized algorithms for low-rank tensor decompositions in the Tucker format

SIAM journal on mathematics of data science

Rachel Minster

Arvind K Saibaba

Misha E Kilmer

2020

Don’t Matricize, Tensorize: Tensor-tensor Products for Optimal Representation and Compression

XXI Householder Symposium on Numerical Linear Algebra

Misha E Kilmer

Lior Horesh

Haim Avron

Elizabeth Newman

2020/6/14

See List of Professors in Misha Kilmer University(Tufts University)