Research Output per year

## Personal profile

### Biography

A specialist of probability theory and its applications, Gérard Ben Arous arrived to NYU's Courant Institute as a Professor of Mathematics in 2002. He was appointed Director of the Courant Institute and Vice Provost for Science and Engineering Development in September 2011. A native of France, Professor Ben Arous studied Mathematics at École Normale Supérieure and earned his PhD from the University of Paris VII (1981). He has been a Professor at the University of Paris-Sud (Orsay), at École Normale Supérieure, and more recently at the Swiss Federal Institute of Technology in Lausanne, where he held the Chair of Stochastic Modeling. He headed the department of Mathematics at Orsay and the departments of Mathematics and Computer Science at École Normale Supérieure. He also founded a Mathematics research institute in Lausanne, the Bernoulli Center. He is the managing editor (with Amir Dembo, Stanford) of one of the main journals in his field, Probability Theory and Related Fields.
Professor Ben Arous works on probability theory (stochastic analysis, large deviations, random media and random matrices) and its connections with other domains of mathematics (partial differential equations, dynamical systems), physics (statistical mechanics of disordered media), or industrial applications. He is mainly interested in the time evolution of complex systems, and the universal aspects of their long time behavior and of their slow relaxation to equilibrium, in particular how complexity and disorder imply aging. He is a Fellow of the Institute of Mathematical Statistics (as of August 2011) and an elected member of the International Statistical Institute. He was a plenary speaker at the European Congress of Mathematics, an invited speaker at the International Congress of Mathematics, received a senior Lady Davis Fellowship (Israel), the Rollo Davison Prize (Imperial College, London) and the Montyon Prize (French Academy of Sciences).

## Fingerprint Weighted list of dominant concepts in the researcher's publications (titles and abstracts).

- 4 Similar Profiles

Spin Glass
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Large Deviations
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Trap
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Large Deviation Principle
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Random Matrices
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Langevin Dynamics
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Converge
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Random walk
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## Research Output 1982 2019

## Complex Energy Landscapes in Spiked-Tensor and Simple Glassy Models: Ruggedness, Arrangements of Local Minima, and Phase Transitions

Ros, V., Ben Arous, G., Biroli, G. & Cammarota, C., Jan 4 2019, In : Physical Review X. 9, 1, 011003.Research output: Contribution to journal › Article

Open Access

ruggedness

tensors

energy

glass

rice

## Scaling limit for the ant in a simple high-dimensional labyrinth

Ben Arous, G., Cabezas, M. & Fribergh, A., Jun 1 2019, In : Probability Theory and Related Fields. 174, 1-2, p. 553-646 94 p.Research output: Contribution to journal › Article

Brownian Excursion

Branching Random Walk

Simple Random Walk

Scaling Limit

Rescaling

## Scaling Limit for the Ant in High-Dimensional Labyrinths

Ben Arous, G., Cabezas, M. & Fribergh, A., Apr 1 2019, In : Communications on Pure and Applied Mathematics. 72, 4, p. 669-763 95 p.Research output: Contribution to journal › Article

Brownian movement

Scaling Limit

Critical Graph

High-dimensional

Higher Dimensions

## Stable Limit Laws for Reaction-Diffusion in Random Environment

Ben Arous, G., Molchanov, S. & Ramírez, A. F., Jan 1 2019,*Probability and Analysis in Interacting Physical Systems - In Honor of S.R.S. Varadhan, 2016.*Olla, S., Friz, P., König, W. & Mukherjee, C. (eds.). Springer New York LLC, p. 123-171 49 p. (Springer Proceedings in Mathematics and Statistics; vol. 283).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Limit Laws

Random Environment

Reaction-diffusion

Fluctuations

Rank One Perturbation

## The Landscape of the Spiked Tensor Model

Ben Arous, G., Mei, S., Montanari, A. & Nica, M., Jan 1 2019, (Accepted/In press) In : Communications on Pure and Applied Mathematics.Research output: Contribution to journal › Article

Tensors

Tensor

Polynomials

Maximum likelihood

Critical point