Eran Treister

Welcome to my website!

I am a faculty member at the Computer Science Department at Ben Gurion University of the Negev, Beer Sheva, Israel. Before that I completed my post doctoral fellowship at the Department of Earth, Ocean and Atmospheric Sciences in the University of British Columbia, Vancouver (2014-2016). There I worked with Prof. Eldad Haber on geophysical inverse problems.

I obtained my PhD from the Computer Science Department at the Technion (2014), under the supervision of Prof. Irad Yavneh. My dissertation: "*Aggregation-based adaptive algebraic multigrid for sparse linear systems*."

Research interests:

My broad research field is computational science (or scientific computing). I am interested in developing efficient numerical solution techniques for various large-scale mathematical problems, such as parameter estimation and data-fitting inverse problems, non-linear optimization problems, solutions of partial differential equations, and sparse linear systems. The problems that I consider arise from diverse fields such as machine learning, signal/image processing, and geophysical imaging, for which I develop efficient parallel algorithms and software for solving the problems at hand efficiently, concerning computing time and energy consumption. Such efficient and scalable algorithms and software are particularly important these days, as the available data get larger in various applications and more sophisticated and better algorithms are required for analyzing them.

Editorial Board Member:

SIAM Journal on Scientific Computing (SISC).

Current and near-future research projects:

Graph neural networks and graph learning.

Accelerating and understanding deep learning: That includes efficient parametrizations of CNNs, understanding stability issues in DL, efficient prunning and sparsity-based techniques, quantization and compression of CNNs, optimization techniques for CNNs.

Seismic imaging: robust and efficient solution to seismic full waveform inversion (FWI) and optical diffraction tomography (ODT), possibly using deep learning.

Efficient methods for elastic and acoustic wave modelling in frequency domain. Including Deep Learning approaches. Multi-preconditioning.

Graph Neural Networks for accelerating solution of PDEs with spatially adaptive meshes in conjunction with Finite Elements Methods.

3D shape reconstruction and model regularization using parametric level set methods.

Teaching:

Winter 2023/4: Numerical Methods for PDEs.

Winter 2023/4: Mini-project in Scientific Computing.

Spring 2024: Optimization Methods with Applications.

Spring 2024: Introduction to Data Science (Joint course with optimization).

Spring 2024: Mini-course: Sparse Optimization.

Contact Information:

email: erant at cs.bgu.ac.il

Dept of Computer Science,

Building 37 Office 206,

Ben Gurion University of the Negev, P.O 653, Beer Sheva, Israel, 8410501.

What's new?

Mar 2024: Accepted to SISC: Bar Lerer, Ido Ben-Yair, Eran Treister, *Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layers*(arXiv)

Mar 2024: Accepted to SISC: Moshe Eliasof, Eldad Haber, Eran Treister, *Graph Neural Reaction Diffusion Models*

Mar 2024: Two new students, Eshed Gal and Raz Monsonego, have joined the group

Mar 2024: Maor Ashkenazi passed his PhD candidacy exam.

Feb 2024: New paper online: Moshe Eliasof, Eldad Haber, Eran Treister, *An Over Complete Deep Learning Method for Inverse Problems*, 2024, (arXiv)

Jan 2024: Paper accepted to AISTATS: Moshe Eliasof, Eldad Haber, Eran Treister, *On The Temporal Domain of Differential Equation Inspired Graph Neural Networks* (arXiv)

Jan 2024: Paper accepted to AISTATS: Yakov Medvedovsky, Eran Treister, Tirza Routtenberg, *Efficient Graph Laplacian Estimation by Proximal Newton*, (arXiv)

Jan 2024: I joined the editorial board of SIAM Journal on Scientific Computing.

Dec 2023: Bar Lerer passed his MSc exam.

Dec 2023: Accepted to AAAI: Moshe Eliasof, Eldad Haber, Eran Treister, *Feature Transportation Improves Graph Neural Networks*

Dec 2023: Accepted to SISC: Rachel Yovel, Eran Treister, *LFA-tuned matrix-free multigrid method for the elastic Helmholtz equation*, (arXiv)

Nov 2023: Racheli Yovel joins the Ariane De Rothschild women Doctorate program.

Nov 2023: Accepted to Inverse Problems: Moshe Eliasof, Eldad Haber, Eran Treister, *DRIP: Deep Regularizers for Inverse Problems*, (arXiv)

Nov 2023: Accepted to Learning on Graphs (extended abstract): Moshe Eliasof, Eldad Haber, Eran Treister, *Advection Diffusion Reaction Graph Neural Networks for Spatio-Temporal Data*, Learning on Graphs 2023.

Nov 2023: Accepted to JCP: Eran Treister and Rachel Yovel, *A Hybrid Shifted Laplacian Multigrid and Domain Decomposition Preconditioner for the Elastic Helmholtz Equations*. (arXiv)

Nov 2023: Moshe Eliasof has submitted his PhD dissertation and moved to a post-doc in Cambridge.

Oct 2023: Accepted to SISC: Gal Shalom, Eran Treister, Irad Yavneh, *pISTA: preconditioned Iterative Soft Thresholding Algorithm for Graphical Lasso*. (arXiv)

Aug 2023: New MSc students join the group: Eshed Gal, Shakked River, and Tslil Sardam (last two are jointly supervised with Prof. Tirza Routtenberg).

Aug 2023: Bar Lerer submitted his MSc thesis.

Aug 2023: Benjamin Bodner passed his MSc exam.

Aug 2023: I received the ISF grant.

Jul 2023: I got tenured at BGU.

Jul 2023: New paper online: Moshe Eliasof, Eldad Haber, Eran Treister, *ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks* (arXiv)

Jul 2023: New paper online: Rachel Yovel, Eran Treister, *LFA-tuned matrix-free multigrid method for the elastic Helmholtz equation* (arXiv)

Jul 2023: New paper online: Bar Lerer, Ido Ben-Yair, Eran Treister, *Multigrid-Augmented Deep Learning for the Helmholtz Equation: Better Scalability with Compact Implicit Layers*. (arXiv)

May 2023: Accepted to IEEE TNNLS: Moshe Eliasof, Benjamin Bodner, Eran Treister, * Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks*. (arXiv)