Babak Rahmani

I am a PhD student in Electrical Engineering department of EPFL where I am advised by Christophe Moser. I also work closely with Demetri Psaltis on Machine learning and its applications in inverse problems, information retrieval and control systems. Prior to joining EPFL, I graduated from Sharif University of Technology, Iran with a M.Sc. and Tehran University, Iran with a B.Sc. both in Electrical Engineering. My goal is to build robust and reliable machine learning algorithms for controlling real-world complex systems in various disciplines ranging from communication, imaging systems to neuroscience and robotics.Please visit my Google Scholar (link above) for an updated list of my publications.

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Multi-mode optical fiber

Multi-mode optical fiber is a type of optical fiber mostly used for communication over short distances, such as within a building or on a campus. Multi-mode links can be used for data rates up to 10

Amplitude

En physique classique, on nomme amplitude la mesure scalaire (une coordonnée) d’un nombre positif caractérisant l’ampleur des variations d'une grandeur. Le plus souvent il s'agit de l'écart maximal

Apprentissage profond

L'apprentissage profond ou apprentissage en profondeur (en anglais : deep learning, deep structured learning, hierarchical learning) est un sous-domaine de l’intelligence artificiel

Learning of physical systems: from inference to control

Babak Rahmani

To characterize a physical system to behave as desired, either its underlying governing rulesmust be known a priori or the system itself be accurately measured. The complexity of fullmeasurements of the system scales with its size. When exposed to real-world conditions, suchas perturbations or time-varying settings, the system calibrated for a fixed working conditionmight require non-trivial re-calibration, a process that could be prohibitively expensive, inefficientand impractical for real-world use cases.In this thesis, a learning procedure for solving highly ill-posed problems of modeling a system'sforward and backward response functions is proposed. In particular, deep neural networksare used to infer the input of a system from partial measurements of its outputs or to obtain adesired target output from a physical system.I showcase the applicability of the proposed methods for inference and control in opticalmultimode fibers. Amplitude/phase-encoded input of a multimode fiber is reconstructedfrom intensity-only measurements of the outputs. Conversely, the required input of the fiberfor projecting a desired output is obtained using intensity-only measurements of the output.Next, the stochastic neural network of the retina in Salamander is modeled by a probabilisticneural network. The model is used to optimize the input stimuli so as to find the simplestspatiotemporal patterns that elicit the same neuronal spike responses as those elicited byhigh-dimensional stimuli.As demonstrated in this thesis, application of data-driven methods for characterization ofcomplex large-scale real-world systems has proved useful in simplifying the measurement apparatus,end-to-end optimization of the system and automatic compensation of perturbation.

EPFL2022

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Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media

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

The output of physical systems, such as the scrambled pattern formed by shining the spot of a laser pointer through fog, is often easily accessible by direct measurements. However, selection of the input of such a system to obtain a desired output is difficult, because it is an ill-posed problem; that is, there are multiple inputs yielding the same output. Information transmission through scattering media is an example of this problem. Machine learning approaches for imaging have been implemented very successfully in photonics to recover the original input phase and amplitude objects of the scattering system from the distorted intensity diffraction pattern outputs. However, controlling the output of such a system, without having examples of inputs that can produce outputs in the class of the output objects the user wants to produce, is a challenging problem. Here, we propose an online learning approach for the projection of arbitrary shapes through a multimode fibre when a sample of intensity-only measurements is taken at the output. This projection system is nonlinear, because the intensity, not the complex amplitude, is detected. We show an image projection fidelity as high as similar to 90%, which is on par with the gold-standard methods that characterize the system fully by phase and amplitude measurements. The generality and simplicity of the proposed approach could potentially provide a new way of target-oriented control in real-world applications when only partial measurements are available.

SPRINGERNATURE2020

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All-fiber spatiotemporally mode-locked laser with multimode fiber-based filtering

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

We demonstrate the first all-fiber multimode spatiotemporally mode-locked laser. The oscillator generates dissipative soliton pulses at 1036 nm with 12 mW average power, 6.24 ps duration, and 24.3 MHz repetition rate. The reported pulse energy (0.5 nJ) represents -4 times improvement over the previously reported single-mode all-normal dispersion mode-locked lasers with multimode interference-based filtering. Numerical simulations are performed to investigate the cavity and spatiotemporal mode-locking dynamics. The all-fiber oscillator we present shows promise for practical use since it can be fabricated simply. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

OPTICAL SOC AMER2020

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