Publications de Babak Rahmani | EPFL Graph Search

An actor-model framework for visual sensory encoding

Demetri Psaltis, Diego Ghezzi, Christophe Moser, Babak Rahmani

A fundamental challenge in neuroengineering is determining a proper artificial input to a sensory system that yields the desired perception. In neuroprosthetics, this process is known as artificial sensory encoding, and it holds a crucial role in prostheti ...

Nature Portfolio2024

Backpropagation-free training of deep physical neural networks

Romain Christophe Rémy Fleury, Ali Momeni, Babak Rahmani, Matthieu Francis Malléjac, Marc Philipp Del Hougne

Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that ...

American Association for the Advancement of Science2023

Learning to image and compute with multimode optical fibers

Demetri Psaltis, Christophe Moser, Babak Rahmani, Ugur Tegin

Multimode fibers (MMF) were initially developed to transmit digital information encoded in the time domain. There were few attempts in the late 60s and 70s to transmit analog images through MMF. With the availability of digital spatial modulators, practica ...

WALTER DE GRUYTER GMBH2022

Learning of physical systems: from inference to control

Babak Rahmani

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

EPFL2022

Roadmap on wavefront shaping and deep imaging in complex media

Demetri Psaltis, Christophe Moser, Babak Rahmani

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow us to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. ...

IOP Publishing Ltd2022

Spatial self-beam cleaning in spatiotemporally mode-locked fiber lasers

Demetri Psaltis, Christophe Moser, Eirini Kakkava, Babak Rahmani, Ugur Tegin

IEEE2021

Full characterization of partially measured systems with neural networks

Demetri Psaltis, Christophe Moser, Navid Borhani, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin

IEEE2021

Learning to See and Compute through Multimode Fibers

Demetri Psaltis, Christophe Moser, Navid Borhani, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin

We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement meth ...

IEEE2021

Deep Learning-Based Image Classification through a Multimode Fiber in the Presence of Wavelength Drift

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava, Babak Rahmani, Ugur Tegin

Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wa ...

2020

Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava, Babak Rahmani, Ugur Tegin

Spatiotemporal nonlinear interactions in multimode fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear interaction of different pump modes from quasi-continuous wave to ultrashort pulses centered around visible to i ...

2020