Research Labs

Yonatan (Yoni) Savir

At the Savir lab, our goal is to understand aging and failure of biological and complex systems. To achieve this goal, we develop AI and machine learning tools, mathematical models, and biophysical models.

Reut Shalgi

Systems Biology approaches to Proteostasis and Neurodegeneration.

The Shalgi lab explores how stress regulation, aging and neurodegenerative diseases are all connected, in order to reveal: how can we harness stress regulation to battle disease?

Noam Kaplan

The Kaplan lab combines experimental and computational methods to study how the 3D organization of the genome is encoded and how it mediates biological function in health and disease.

Hadas Benisty

The Benisty lab studies interpretable dynamic models for neuronal networks for learning processes and degenerative diseases.

Omri Barak

The Barak lab explores the hypothesis that systems that learn can be useful models of one another. This is because of general principles that seem to transcend specific instances, such as multiplicity of solutions, low-rank perturbations and more.

Dori Derdikman

How does the brain represent space? How do we navigate? Our lab researches the brain algorithms for orienting and spatial memory, trying to understand how these brain network are formed, how do they change, and how they are read out.

Keren Yizhak

The lab of Computational Cancer Genomics

Ruth Hershberg

The Hershberg lab studies the dynamics of bacterial adaptation under a variety of selective pressures, from extremely prolonged resource exhaustion to growth under antibiotic treatment.

Naama Geva-Zatorsky

The Geva-Zatorsky lab combines computational and experimental approaches to study the microbiome, the hidden microbes living within us, and the ways we harness the microbiome for future diagnostics and therapeutics.

Shai Shen Orr

Systems Immunology and Precision Medicine Lab: Drivers of immune variation, their evolutionary origin and implications for precision medicine.

Ben Engelhard

The Engelhard lab studies the neural circuits in the brain underlying complex behavior and are especially interested in using computational and experimental approaches to understand the algorithms by which the dopamine system in the brain underlies reward based-learning.