PhD in Engineering Sciences, 2013
Université catholique de Louvain
Master in Higher Education Pedagogy, 2010
Université catholique de Louvain
M.Sc. in Computer Science Engineering, 2007
Université catholique de Louvain
Prof. Benoît Frénay obtained his PhD in Machine Learning in 2013, for which he received the Scientific prize IBM Belgium for Informatics 2014. In 2014, he became Assistant Professor at the University of Namur (UNamur) where he is currently (co)supervising a total of 10 PhD students, 3 postdocs and 5 to 10 Master’s theses/year. Two PhD students have already graduated under his (co)supervision, and one postdoc obtained another position at UCLouvain.
In 2018, Prof. Frénay obtained an Excellence of Science grant (success rate of 14.1%) as co-PI for VeriLearn, a large project on verifying AI systems with ten academics from three Belgian universities. In 2019, he obtained BRAIN-BE 2.0 funding as co-PI for the DIGI4FED project. In 2017 and 2019, he obtained two UNamur CERUNA PhD grants as supervisor and co-supervisor, respectively. Since 2019, he has been co-supervising a PhD student employed by AW-EUR in industry 4.0. In 2020 and 2021, he obtained two FRIA PhD grants as co-supervisor and supervisor, respectively. Since 2021, he has been in charge of one of the four scientific work packages in the large ARIAC project by DigitalWallonia4.ai (€32M and several dozens of academics involved), for which he is the representative of UNamur. In 2021, he obtained an ARC grant with Anne-Sophie Libert for the CAML project to analyze the behavior of extrasolar planetary systems with deep learning. In 2021, he obtained a Win2Wal grant on Sensors and MAchine Learning for RemoTe Smart EnviroNmental monitoring SystemS to integrate physical knowledge in Machine Learning models. In 2022, he was promoted to Professor at UNamur.
According to Google Scholar, he has 2398 citations and an h-index of 16, with more than 15 peer-reviewed articles in journals such as ACM Computing Surveys (IF=10.2), Array, Computational Statistics & Data Analysis (IF=1.3), IEEE transactions on cybernetics (IF=10.4), IEEE transactions on artificial intelligence, IEEE transactions on neural networks and learning systems (IF=11.7), Neural Networks (IF=5.8), Neurocomputing (IF=4.1) and Pattern Recognition Letters (IF=3.7) and more than 30 peer-reviewed articles in conferences such as ECML-PKDD (A), ESANN (B), ICANN (C), IJCNN (B), IWANN (B), NeurIPS (A*) and UAI (A) and workshops at EGC (C), ICLR (A*), ICML (A*), ICSE (A*), IJCAI (A*) and NeurIPS (A*). He has published works outside of his research community, e.g., in the Artificial Intelligence and Law and Environment and Planning B: Planning and Design journals, and at the European Conference of the International Federation for Medical and Biological Engineering. He has also been invited as a Machine Learning expert by the Council of Europe to provide recommendations on profiling and on the Convention 108+. In the past five years, he has co-organised three workshops on interpretability and explicability at ECML-PKDD and CIKM and two special sessions on Information Visualization and Machine Learning at ESANN. In 2014, he organised the IBM chair on Data Science.