Hi, I’m Florent, welcome to my personal website!
I am currently a post-doctoral researcher at EPFL in the LMIS2 lab, and a data scientist & software engineer at the Nagi Bioscience start-up. I obtained my PhD in Computer Science in 2021 at LIPN, the CS lab of Université Sorbonne Paris Nord (Paris 13) . My broad areas of interest are unsupervised machine learning and large-scale data-driven applications. I like to develop and apply advanced algorithms on large, complex, industrial data sets. My PhD project was an industry collaboration with Safran Aircraft Engines, a leading aircraft engine manufacturer. Before, I graduated from ISAE-Supaero engineering school in Toulouse .
You can find recent news and events I attend on this page. Feel free to reach out to me via e-mail or LinkedIn for discussions and potential collaborations!
- (December 2021) The second LITSA workshop will take place virtually (again) at ICDM 2021.
- (August 2021) We’re very glad that the paper Deep embedded self-organizing maps for joint representation learning and topology-preserving clustering has been published in Neural Computing and Applications!
- (April 2021) I have joined EPFL to develop the software and data analysis ecosystem around Nagi Bioscience’s revolutionary “worm-on-chip” technology for faster and ethical biological testing.
- (March 2021) After successfully defending my PhD thesis, I’m now a doctor in Computer Science!
- (January 2021) I will defend my PhD thesis on March 22, 2021.
- (November 2020) The first LITSA workshop at ICDM was a success! Thanks to all participants, organizers and committee members.
- (October 2020) Our paper applying the Stadion clustering criterion to transformation-invariant time series is accepted at ICPR 2020!
- (September 2020) Paper on large-scale vibration monitoring accepted at the annual conference of PHM Society 2020!
- (September 2020) I am co-organizing the first workshop on Large-scale Industrial Time Series Analysis (LITSA), hosted by IEEE ICDM 2020, with a top-notch committee!
- (August 2020) skstab, a Python module for clustering stability analysis with a scikit-learn compatible API, is available on Github.
- (June 2020) Our paper introducing a new principle for clustering stability analysis in available on arXiv!
- 1st workshop on Large-scale Industrial Time Series Analysis (LITSA) @ IEEE ICDM 2020, November 17, virtual [organizer]
- Annual conference of the PHM Society, November 9-13, virtual [presented a paper]
- AGIFORS Symposium, October 20-23, virtual [presented a paper]
- CAp: Conférence d'Apprentissage, June, virtual [published a French version of a previous paper]
- Applied Machine Learning Days, January 26-28, Lausanne, Switzerland
- EGG Paris dataiku, November 7, Paris, France
- France is AI, October 23, Paris, France
- International Workshop on Machine Learning & Artificial Intelligence, October 7-8, Paris, France
- Climate Informatics, October 3-4, Paris, France
- LDRC (Learning Data Representation for Clustering) workshop @ PAKDD 2019, April 14-17, Macau, China [presented a paper]
- ESANN (European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning), April 24-26, Bruges, Belgium [presented a paper]
- TS days (Journées sur les données temporelles), March 25-26, Rennes, France
- Applied Machine Learning Days, January 26-29, Lausanne, Switzerland
- IEEE International Conference on Big Data, December 10-13, Seattle, USA [presented a paper]
- IEEE SC2-IoV-SOCA tutorials day, November 19, Paris, France
- Deep Learning Day, September 14, Winterthur, Switzerland
- DS3 (Data Science Summer School), June 25-27, Palaiseau, France
- S4D (Research Summer School on Statistics for Data Science), June 18-22, Caen, France