Florent Forest

Florent Forest
École Polytechnique Fédérale de Lausanne | EPFL · Institute of Microengineering

PhD

About

13
Publications
4,615
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
128
Citations
Additional affiliations
January 2018 - March 2021
Safran Aircraft Engines
Position
  • Analyst
Description
  • PhD student
Education
January 2018 - March 2021
Université Paris 13 Nord
Field of study
  • Computer Science
September 2013 - October 2017
Institut Supérieur de l'Aéronautique et de l'Espace (ISAE)
Field of study
  • Aerospace Engineering, Data Science
September 2011 - July 2013
Lycée Janson-de-Sailly
Field of study

Publications

Publications (13)
Article
Full-text available
A recent research area in unsupervised learning is the combination of representation learning with deep neural networks and data clustering. The success of deep learning for supervised tasks is widely established. However, recent research has demonstrated how neural networks are able to learn representations to improve clustering in their intermedi...
Thesis
Full-text available
This thesis is interested in unsupervised statistical learning methods and their applications to health monitoring of aircraft engines at an industrial scale. Our first objective is to make health monitoring methodologies scale to massive datasets and allow engineering team to flexibly deploy various use cases. Besides the engineering aspects, we a...
Conference Paper
Full-text available
Time series clustering is a challenging task due to the specificities of this type of data. Temporal correlation and invariance to transformations such as shifting, warping or noise prevent the use of standard data mining methods. Time series clustering has been mostly studied under the angle of finding efficient algorithms and distance metrics ada...
Preprint
Full-text available
Self-Organizing Map algorithms have been used for almost 40 years across various application domains such as biology, geology, healthcare, industry and humanities as an interpretable tool to explore, cluster and visualize high-dimensional data sets. In every application, practitioners need to know whether they can \textit{trust} the resulting mappi...
Conference Paper
Full-text available
Vibration analysis is an important component of industrial equipment health monitoring. Aircraft engines in particular are complex rotating machines where vibrations, mainly caused by unbalance, misalignment, or damaged bearings, put engine parts under dynamic structural stress. Thus, monitoring the vibratory behavior of engines is essential to det...
Preprint
Full-text available
Model selection is a major challenge in non-parametric clustering. There is no universally admitted way to evaluate clustering results for the obvious reason that there is no ground truth against which results could be tested, as in supervised learning. The difficulty to find a universal evaluation criterion is a direct consequence of the fundament...
Chapter
Full-text available
Recent research has demonstrated how deep neural networks are able to learn representations to improve data clustering. By considering representation learning and clustering as a joint task, models learn clustering-friendly spaces and achieve superior performance, compared with standard two-stage approaches where dimensionality reduction and cluste...
Poster
Full-text available
Poster for the USPN Galilée graduate school day 2019 (Journée de l'école doctorale Galilée)
Conference Paper
Full-text available
In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map. Our model is composed of an autoencoder and a custom SOM layer that are optimized in a joint training procedure, motivated by the idea th...
Conference Paper
Full-text available
Recent research has demonstrated how deep neural networks are able to learn representations to improve data clustering. By considering representation learning and clustering as a joint task, models learn clustering-friendly spaces and achieve superior performance, compared with standard two-stage approaches where dimensionality reduction and cluste...
Article
Full-text available
The field of gamma-ray astronomy has seen important progress during the last decade, yet there exists so far no common software framework for the scientific analysis of gamma-ray telescope data. We propose to fill this gap by means of the GammaLib software, a generic library that we have developed to support the analysis of gamma-ray event data. Ga...
Article
Full-text available
List of contributions from the CTA Consortium presented at the 34th International Cosmic Ray Conference, 30 July - 6 August 2015, The Hague, The Netherlands.

Network

Cited By

Projects

Project (1)
Project
The goal of this project is to go forward into the analysis of scalar, binary, and mixt data in order to apply clustering algorithm to massive datasets. Then succeed to find the right intern and/or similarity index in order to guaranty the quality of the clustering.