Jan Ramon

Jan Ramon is a senior researcher at INRIA Lille Nord Europe, France, and a core member of the MAGNET (Machine learniNG in large-scale information NETworks) research team. His work spans both theoretical and applied aspects of machine learning, with a focus on graph-structured data and privacy-preserving AI. He is actively leading multiple high-impact research projects in decentralized and trustworthy machine learning.

His research interests include:

• Data Mining and Machine Learning on Graphs
• Algorithmic and Statistical Analysis of Network Data
• Privacy-Preserving Techniques (e.g., Federated Learning, Differential Privacy)
• Secure Multi-Party Computation
• Applications in Biomedicine and Traffic
• Explainable and Fair AI

His research projects, such as REDEEM, FLUTE, TRUMPET, TIP, and TAILED, demonstrate a strong trend toward building resilient, decentralized, and privacy-preserving AI systems. These projects often involve cross-border collaborations and aim to exceed GDPR compliance while enabling impactful applications in healthcare, particularly in oncology and prostate cancer diagnosis. The work integrates theoretical rigor with practical deployment, often contributing to open-source libraries like TAILED.

Jan Ramon is a principal investigator (PI) on several major grants funded by ANR, Horizon Europe, and INRIA. He supervises PhD students and collaborates with engineers and post-doctoral researchers. He is also a member of the editorial boards of the Machine Learning and Data Mining and Knowledge Discovery journals.

He has advised or is currently advising the following students:

• Antoine Barczewski (PhD student)
• Marc Damie (PhD student)

His research is supported by significant grants and collaborations with institutions across Europe, including KU Leuven, CEA, CNRS, and various European universities and hospitals. He leads the development of open-source tools for trustworthy AI and is deeply involved in setting standards for privacy in federated learning.

Jan Ramon leads or has led the following research projects:

• REDEEM (PEPR IA, ANR-23-PEIA-0005, 2023–2027): Resilient, Decentralized, Privacy-Preserving ML
• FLUTE (Horizon Europe, 2023–2025): Federated Learning for Prostate Cancer
• TRUMPET (Horizon Europe, 2022–2025): Trustworthy Multi-site Privacy Enhancing Technologies
• TIP (2021–2025): Transparent AI preserving Privacy
• ADT-TAILED (2020–2023): Trustworthy AI Library for Decentralized Environments
• ANR PMR (2021–2024)
• ERC PoC SOM (2016–2018)
• IWT-SBO InSPECtor (2014–2017)
• Chist-ERANET Adalab (2015–2017)