Prof. Hugues Garnier


Phone: +33 (0)3 72 74 68 33 or +33 (0)6 95 41 98 56

Email: hugues.garnierATuniv-lorraine.fr

Université de Lorraine
Polytech Nancy - CRAN
2 rue Jean Lamour
54519 Vandoeuvre-les-Nancy Cedex

Citations on Google scholar

Latest News

- The World's top 2% of top scientists (2022) - My name is on this prestigious list !

I'm thrilled to be in the "2022 World Ranking of Top 2% Scientists/Researchers" for career-long impact, announced on 28 October 2022. Stanford University has recently published an update of the list of the top 2% most widely cited scientists in different disciplines. This ranking, considered the most prestigious worldwide, is based on the bibliometric information contained in the Scopus database and includes more than 190,000 researchers from the more than 8.5 million scientists considered to be active worldwide, with 22 scientific fields and 176 subfields taken into account. I would like to thank my colleagues, PhD students, friends and everyone who helped me a lot to get this achievement!

Access to the list: https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/

- Member of the Scientific Committee of the 6th Doctoral School on Data-driven Model Learning of Dynamical Systems, 3-7 April, Lyon, 2023.

- Our project proposal with Chili has been selected by the ECOS-Sud Programme. The collaboration starts in 2022 for 3 years. The goal is to develop deep learning-based methods for identifying continuous-time nonlinear systems.

A new version of the CONTSID toolbox for Matlab has been released in July 2021. The main addition is a new Graphical User Interface (GUI).

- A new collaboration with Dassault Aviation has started in Autumn 2020 for three years on "Data-driven nonlinear system identification and health state prognostics using deep learning. Application to predictive maintenance of business jet aircraft". More details can be found on the CRAN website.

- I have served as opponent at Rodrigo González's defense for his Licentiate Thesis entitled "Consistency and efficiency in continuous-time system identification". The defense took place in June, 2020 at KTH Royal Institute of Technology, Stockholm.