Welcome to
Basic Theoretical Research in the Mathematical & Natural Sciences
THE REUVENI GROUP
March 24, 2026
Congratulations to Tommer David Keidar!
Congratulations to Tommer David Keidar for winning the Adamas Fellowship of the Israel Academy of Sciences and Humanities.
August 6, 2025
New Paper: Adaptive resetting for informed search strategies and the design of NESS
We introduce a general framework for adaptive resetting, where stochastic resetting depends on the state and age of the process. Using a reweighting scheme applied to trajectories without resetting, we efficiently compute key observables like first-passage times and steady-states. This enables the design of informed search strategies and complex non-equilibrium behaviors without brute-force sampling. We further develop a machine learning approach to optimize adaptive resetting protocols and demonstrate its effectiveness in accelerating molecular dynamics simulations.
March 23, 2025
Congratulations to Tommer David Keidar!
Congratulations to Tommer David Keidar for winning the David and Paulina Trotsky Foundation Fellowship for the school year 2024-25.
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February 8, 2026
New Paper: Measurements and simulations of transmembrane water exchange by diffusion NMR methods
We compare CG-PFG and FEXSY for measuring transmembrane water exchange in biological samples, from yeast cells to optic nerve tissues. While the two methods agree qualitatively, they can differ quantitatively, and the standard bicompartmental exchange model can fail in complex tissues. We introduce a tricompartmental framework that captures an additional restricted, non-exchanging, water pool and revise the inferred exchange times.
June 4, 2025
New Paper: First-passage approach to optimizing perturbations for improved training of ML models
Machine learning models have become indispensable tools in applications across the physical sciences. However, their training is often time-consuming. Several protocols have been developed to perturb the learning process and improve the training. However, their design is usually done ad hoc by intuition and trial and error. To rationally optimize training protocols, we frame them as first-passage processes. This reveals that a model’s response at a single perturbation frequency can predict its behavior across others.
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March 20, 2025
New Paper: High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis
We derive high-order Michaelis-Menten equations that extend the classical linear relation between mean turnover time and reciprocal substrate concentration. These equations reveal universal linear relations for higher-order moments, granting access to previously inaccessible kinetic parameters. We show how key observables—such as the enzyme-substrate lifetime, the binding rate, and the probability of successful catalysis—can all be inferred from these relations.
October 16, 2025
Congratulations to Bara Levit!
Congratulations to Bara Levit for winning the Outstanding Female Students, in High-Tech Fields, Scholarship of the Council for Higher Education. ​​​​​​
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March 26, 2025
Congratulations to Itamar Shitrit!
Congratulations to Itamar Shitrit for winning the outstanding student's scientific achievement award of the center for Physics and Chemistry of living systems at Tel Aviv University. The prize was awarded for his research – Sokoban percolation on the Bethe lattice – which was carried out in collaboration with Ofek Lauber Bonomo.
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February 19, 2025
Congratulations to Tommer David Keidar!
Congratulations to Tommer David Keidar for winning the outstanding poster award as part of the 88th annual meeting of the Israel Chemical Society. The award was given for his research on the universal linear response of the mean first-passage time.
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