PhD seminars – 2022/2023

PhD seminars – 6 february, 2023

Round table: The potential careers at inria after the PhD

We don’t always know what we want to do after our PhD. This round table is meant to present the potential positions you can held at INRIA after your PhD.

First Manuel Serrano, from the Comité d’Evaluation (CE), will present the competitive examinations to become a researcher or engineer at inria and the purpose of the CE. In short, the CE is:

  • in charge of organizing Inria’s teams evaluation
  • in charge of running all the national recruiting and promotion juries.
  • participates in all local recruiting campaigns.

Then, Anthony Schoof, the Head of Transfer, Innovation and Partnerships will present the Inria Start-up Studio program and its benefits!

Finally, we have also invited several new engineers, researchers and members of the start-up studio to share their experiences with us:

  • Jenny Kartsaki: Research and Development Engineer at the NeuroMod Institute, Université Cote d’Azur in collaboration with the SED (Service d’Expérimentation et de Développement) Inria
  • Côme Lebreton: Engineer in ABS team
  • Romain Tetley: Engineer transfert and Innovation
  • Francesca Casagli: Permanent researcher in the BIOCORE team
  • Antonia Machlouzarides-Shalit: previous member of INRIA start-up studio and CEO of NeuroPin

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PhD seminars – 23 january, 2023

Scientific presentation: Walid Djema  – Biocore/Green owl team

Control of bio-processes
Walid Djema

Flash presentations:

Tom Swagier (Epione team)

Tom Szwagier presenting

Kilian Burgi (Masai team)

PhD seminars – 9 january ,2023

Masimba Nemaire – Factas team

Title: Silent L^p magnetisations

Abstract:

In paleo-magnetic and geo-magnetic studies, under the quasi-static approximation of Maxwell’s equations, magnetizations inside rocks are recovered from measurements of magnetic fields outside rock samples.

Masimba Nemaire

This problem is ill-posed as there exist magnetizations that produce null magnetic fields outside the rock samples, these magnetizations are called silent magnetizations. We model magnetizations as [Lp(Ω)]n-vector-fields for n 3, 1 < p < with Rn a bounded open set. Using the Helmholtz decomposition of [Lp(Rn)]n we characterize silent
magnetizations. Any two magnetizations that differ by a silent magnetization are said to be equivalent magnetizations, using dual maximization problems and duality mappings we characterize norm-minimizing equivalent magnetizations given any [Lp(Ω)]n -magnetization.
Using the characterizations of silent and norm-minimizing equivalent magnetizations, we extended the classical Helmholtz decomposition of [Lp(Ω)]n-vector-fields to a three-term decomposition that holds for any open domain and for 1 < p < . In the case n=3, that is C1,1 and simply connected, we propose an iterative procedure that solves a p-Laplace Neumann and a p-curl-curl system to get the norm-minimizing equivalent magnetization given any magnetization in [W 1,p(Ω)]3. In this case the norm-minimising equivalent magnetization can be approximated using Galerkin methods.

Flash presentations:

Alesia Herasimenka – Laureate Lafitte Prize 2022

Alesia Herasimenka presenting the Laffitte Prize she won

PhD seminars – 5 december, 2022

Harikrishna Kuttivelil  –  Diana team

Exploring community structures in the field of decentralised artificial intelligence

Abstract:As the number of connected devices continues to increase exponentially and intelligent applications and services continue to permeate these devices, the network edge has become a critical space for communication and intelligence infrastructures. The symbiotic relationship at the intersection of edge computing and artificial intelligence has resulted in the emergence of a new field, edge intelligence, gaining prominence in many domains, including IoT, sensor networks, autonomous vehicles, and more. Yet, intelligence networks at the edge face unique challenges compared to those at the network core, including network volatility, instability, and other communication challenges.

Hari

In this research, we consider the decentralization of intelligence networks as an enabling technology for edge intelligence while being cognizant of both the networking and learning aspects of such systems. To this end, we aim to exploit the evolving network and application affinities of the agents in a decentralized learning network to dynamically form communities of collaboration, in which agents collaborate on training and inference. In doing so, we are developing a framework to facilitate this process. As we apply this framework to real-world applications such as next-word prediction on mobile devices and environmental monitoring on heterogeneous sensor networks, the question arises: how can we evaluate such systems? As such, we also aim to answer this question by developing simulation platforms and tools that allow us to model both applications and networks to properly understand the overall performance of these systems and the interactions between the application and networking aspects.

Flash presentations:

David Loiseaux – Datashape team

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Adel Malik Annabi –  McTao team

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PhD seminars – 21 November, 2022

Mamoutou Diarra – Inria DIANE team

Titre: Mécanismes améliorés de la couche de transport pour les réseaux cellulaires assistés par MEC.

Abstract: Addressing transport-layer issues in cellular networks is still a hot research topic, despite the fact that a wide range of solutions have been proposed over the last decades. One reason that could explain this phenomenon is the difficulties in the real-world adoption of the proposed solutions as they require either drastic changes in the cellular network protocol stack (in the UE and/or in the base station) or a standardization effort so that TCP headers can include radio information. Another reason is the fact that most of these solutions are effective only in a few scenarios and fail to mitigate the transport-layer issues considered on a global or large scale.

 
Mamoutou Diarra
Inspired by these limitations and the growing trend towards Mobile and Multi-Access Edge Computing (MEC), we set out in this thesis to demonstrate that several transport-layer issues in
a MEC-enabled 4G/5G network can be mitigated without requiring any modifications in the end-user’s device or in the 4G/5G stack. To this end, we propose novel transport-layer optimization solutions that leverage the MEC Radio Network Information Service (RNIS) and other MEC capabilities to improve some key traditional transport-layer mechanisms such as slow start, steady state behavior, loss detection and flow control mechanisms. We show the efficiency of this approach by first proposing SIGMA, an uplink-oriented Congestion Control Algorithms (CCA) that outperforms BBR and existing CCAs in terms of throughput and delay; then we propose and implement MELD, a loss discrimination algorithm that can improve the throughput of loss-based
CCAs by up to 80% in case of wireless/random losses; and lastly we propose RAPID, a RAN-aware Performance Enhancing Proxy (PEP) capable of transparently reducing the increase in delay
by a factor of 10 to 50 without lowering the throughput, regardless of the number or the behaviors of the TCP flows sharing the same per-user buffer. Finally, we implement MELD and RAPID
in Linux and validate their performance in a real-world 4G network based on OpenAirInterface and FlexRAN.
 
 
Keywords: Cellular networks, Multi-access Edge Computing, Congestion Control Algorithms, Transport-layer issues, TCP optimization, Radio Network Information Service
 
 

Flash presentations:

Nicolas Valade – Calisto team

 

PhD seminars – 7 November, 2022

Bernard-Tamba Sandouno – Équipe DIANE

Title: Accurate Mobile signal Power estimation

Abstract:

Ray Tracing is a propagation modelling approach that accurately estimates the signal power received by end users while taking into account the details of the environment in their vicinity. This accuracy is at the cost of high computational load and high memory consumption due to the heavy computation performed by processes such as Ray Generation. In this presentation, we will introduce a site-specific ray generation technique that is able to generate up to 1 million rays within 5 seconds and a root mean square error for bandwidth estimation within 2 Mbps.

Bernard Samba Sandouno

Depending on the location of the antenna and the coverage area, our technique gives the minimum possible number of rays required in order to estimate end-users’ signal power received and their download bitrate.

Flash presentations:

Marielle Péré –  Biocore team

Modeling heterogeneous cancer drug responses in isogenic single-cells to predict drug-tolerant persisters

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PhD seminars – Organizing team

PhD seminars: new formula!

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