Vita

Short summary of occupied positions :
2020-present Full Professor, ISAE-SUPAERO (Toulouse, France).
2011-2020 Associate Professor, ISAE-SUPAERO (Toulouse, France).
2011 Research engineer, EDF R&D (Clamart, France)
2010 Post-doctoral researcher, University of Liège (Belgium)
2009 Invited researcher, EDF R&D (Clamart, France)
2009 Post-doctoral researcher, Technical University of Crete (Chania, Greece)
2005-2008 PhD candidate, ONERA Toulouse (France)

Qualifications
2020 HDR in Artificial Intelligence (habilitation to supervise research) - University of Toulouse (France)
2008 PhD in Artificial Intelligence - University of Toulouse (France)
2005 MS in Control Theory - University of Toulouse (France)
2005 Generalist and Aeronautics Engineer – SUPAERO (Toulouse, France)

Non-academic
Paragliding instructor (and passionate pilot).

One paragraph
Emmanuel Rachelson is professor of machine learning and optimization at ISAE-SUPAERO. Prior to joining ISAE-SUPAERO (2011), he held a researcher position at EDF Research and Development (2009, 2011) and postdoctoral positions at the University of Liège (2010) and the Technical University of Crete (2009). He earned a PhD in artificial intelligence (2009) and the Habilitation degree (2020) from the University of Toulouse.
As a teacher, he has been involved in promoting artificial intelligence education. He has been responsible for the Intelligent Decision Systems minor track (MS, 2012) and has founded the Data and Decision Sciences major track (MS, 2015) in the ISAE-SUPAERO curriculum. He also co-founded the Artificial Intelligence and Business Transformation executive master program (2021) and co-organized the international Reinforcement Learning Virtual School (2021). He teaches machine learning and optimization to master and PhD students, and in continuing education programs.
His research is in the field of reinforcement learning and related topics. He created the ISAE-SUPAERO Reinforcement Learning Initiative (SuReLI, 2016) which fosters interaction between PhD students, postdocs and permanents researchers on reinforcement learning topics and their interplay with other disciplines. He investigates the reliability of reinforcement learning methods from different points of view such as statistical generalization, robustness to uncertainty, transfer, simulation to reality, etc. He is also interested in the practical applications of reinforcement learning such as fluid flow control, parameter control in optimization problems, unmanned vehicles, air traffic management, software testing, or therapeutic planning.
He is a member of the ACM and the AFIA, is an ANITI member, has published papers and is a reviewer in the main machine learning and artificial intelligence conferences and journals.