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.


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

Dept of Computer Science,
Building 37 Office 206,
Ben Gurion University of the Negev, P.O 653, Beer Sheva, Israel, 8410501.