I am currently a scientist at EPFL in the Intelligent Maintenance and Operations Systems (IMOS) lab. Previously, I was working as a data scientist & software engineer at Nagi Bioscience. I have obtained my PhD in Computer Science in 2021 at LIPN, the CS lab of Université Sorbonne Paris Nord (Paris 13) . My PhD project was an industry collaboration with Safran Aircraft Engines. Before, I graduated from ISAE-Supaero engineering school in Toulouse .
My broad areas of interest are unsupervised and supervised machine learning, with a focus on robustness (domain adaptation), interpretability (XAI) and engineering applications. I also like building large-scale data-driven applications and developing advanced algorithms on complex industrial data sets.
You can download my (maybe outdated) CV.
Feel free to reach out to me via e-mail or LinkedIn for discussions and potential collaborations.
News
- 🎉 Our paper Calibrated Adaptive Teacher for Domain-Adaptive Intelligent Fault Diagnosis has been accepted for publication in the Sensors journal!
- 🎉 Our survey paper on Knowledge-based and Expert Systems in Prognostics and Health Management, written following Kalil's master thesis in our lab, has been published in the International Journal of the PHM Society (IJPHM).
- 🔥 Brace yourselves for the 8th IMC conference and 1st IMC-HOW? Workshops taking place next week on Sept. 2-4!
- 🎉 Two papers accepted at ECCV 2024 workshops Uncertainty Quantification for Computer Vision (UnCV) and Neural Fields Beyond Conventional Cameras
- 🎉 Our paper Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights has been accepted at the ECCV 2024 conference!
- 🎉 Our paper From classification to segmentation with explainable AI: A study on crack detection and growth monitoring has been accepted for publication in Automation in Construction.
- 🎉 Our extended abstract on data augmentation for road damage detection, a result of work done by Punnawat Siripatthiti during his Master thesis, is accepted for presentation at ESREL 2024.
Old
- 🎉 Two extended abstracts on domain adaptation and explainable AI applied to engineering applications are accepted at ESREL 2023.
- 🎉 Our work Selecting the Number of Clusters K with a Stability Trade-off: an Internal Validation Criterion, a collaboration with Alex Mourer during our PhDs, was accepted at the PAKDD 2023 conference.
- ➡️ I have joined Olga Fink's research group IMOS (Intelligent Maintenance and Operations Systems)!
- 📅 The second LITSA workshop will take place virtually (again) at ICDM 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!
- ➡️ 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.
- 🎓 After successfully defending my PhD thesis, I'm now a doctor in Computer Science!
- 📅 I will defend my PhD thesis on March 22, 2021.
- 👏 The first LITSA workshop at ICDM was a success! Thanks to all participants, organizers and committee members.
- 🎉 Our paper applying the Stadion clustering criterion to transformation-invariant time series is accepted at ICPR 2020!
- 🎉 Our paper on large-scale vibration monitoring accepted at the annual conference of PHM Society 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!
- 💾 skstab, a Python module for clustering stability analysis with a scikit-learn compatible API, is available on Github.
- 📰 Our paper introducing a new principle for clustering stability analysis in available on arXiv!
Upcoming events
Recent events
2023
- Intelligent Maintenance Conference, September 12-13, Lausanne, Switzerland [organizer, speaker, session chair]
- ESREL (European Safety and Reliability Conference), September 3-8, Southampton, United Kingdom [presenting 2 extended abstracts]
- PAKDD (Pacific-Asia Conference on Knowledge Discovery and Data Mining), May 25-28, Osaka, Japan [presenting a paper]
2022
- Intelligent Maintenance Conference, September 6-7, Lausanne, Switzerland [session chair]
- Future Labs Live, June 7-8, Basel, Switzerland
- Applied Machine Learning Days, March 27-30, Lausanne, Switzerland
- Winter School on AI for Health (AI4Health), January 10-14, virtual
2021
2020
- 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
2019
- 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 [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
2018
- 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
Research activities
Main areas of interest and application domains:
- Machine learning (supervised, unsupervised and weakly-supervised)
- Domain adaptation
- Explainable AI (XAI)
- Industrial and engineering applications of AI
- Health monitoring of complex industrial systems (made of superalloys or living cells)
- Building end-to-end, large-scale, data-driven applications
- Experience in time series analysis, computer vision and natural language processing
Organizer:
- Intelligent Maintenance Conference (IMC) 2024 ; 2023
- Workshop on Large-scale Industrial Time Series Analysis (LITSA) 2023 ; 2021 ; 2020
- Atelier Mécanismes d’Attention et Apprentissage Automatique (M3A) 2023
Reviewer:
- Journals: Annals of Data Science, Annual Reviews in Control, Engineering Applications of Artificial Intelligence, IEEE Sensors, MDPI Mathematics, Machine Learning, Mechanical Systems and Signal Processing, Neurocomputing, Neural Computing and Applications
- Conferences: CIKM (ACM Int. Conf. on Information and Knowledge Management), ECAI, ESREL, IAI (Industrial AI), LITSA workshops, PHM-Europe
Supervised students (in alphabetical order):
- L. A. (Master thesis 2024/2025, with IMDM SA)
- Kalil Bouhadra (Master thesis 2023/2024, with IMDM SA) – Research intern, McGill University (Canada)
- Étienne Chassaing (Master thesis 2024, co-supervision with Schindler)
- Giada Ehrlich (Master semester project 2024/2025)
- Yan Hao (Master thesis 2022/2023)
- Mariam Hassan (Master thesis 2023/2024, co-supervision with Schindler) – PhD student, EPFL VITA lab (Switzerland)
- Antoine Laborde (Master thesis 2023/2024, co-supervision with Schindler) – AI engineer, inspire AG (Switzerland)
- Uddhava Yann Monney (Master thesis 2024/2025)
- Juliette Parchet (Master thesis 2024/2025, co-supervision with Schindler)
- Esteban Requena (Master semester project 2023/2024) – Civil engineer, Pini group (Switzerland)
- Hana Salvetova (Bachelor semester project 2023/2024)
- Kseniia Shevchenko (Master thesis 2024)
- Punnawat Siripatthiti (Master thesis 2023) – Researcher, Department of Highways (Thailand)
PhD thesis
My PhD thesis is titled “Unsupervised Learning of Data Representations and Cluster Structures: Applications to Large-scale Health Monitoring of Turbofan Aircraft Engines” and was supervised by M. Lebbah, H. Azzag and J. Lacaille. You can find the manuscript and slides on this page.