Scholar
Florent Forest
Google Scholar ID: vw7dDG0AAAAJ
Scientist, EPFL (École Polytechnique Fédérale de Lausanne)
Citations & Impact
Citations
421
H-index
12
i10-index
14
Publications
20
Co-authors
20
list available
Publications
Resume (English only)
Academic Achievements
- Multiple papers accepted for publication in journals such as Sensors, IJPHM, and conferences like ECCV, PAKDD; co-organized several academic conferences and workshops; developed the skstab Python module available on GitHub; completed various research works on domain adaptation, explainable AI, etc.
Research Experience
- Currently a scientist at EPFL's Intelligent Maintenance and Operations Systems (IMOS) lab; formerly worked as a data scientist & software engineer at Nagi Bioscience; involved in multiple research projects.
Education
- Obtained a PhD in Computer Science from Université Sorbonne Paris Nord (Paris 13) in 2021, collaborating with Safran Aircraft Engines; previously graduated from ISAE-Supaero engineering school in Toulouse.
Background
- AI Research Scientist, with broad areas of interest in unsupervised and supervised machine learning, focusing on robustness (domain adaptation), interpretability (XAI), and engineering applications. Enjoys building large-scale data-driven applications and developing advanced algorithms on complex industrial data sets.
Miscellany
- Contactable via email or LinkedIn; participated in organizing academic events such as LITSA.
