POUYA MEHRJOURSSAHT (Student Member, IEEE) received a B.S. degree in electrical engineering from Hamedan University of Technology, Hamadan, Iran, and the M.S. degree in communication fields and waves from Isfahan University of Technology, Isfahan, Iran. He is currently working toward the Ph.D. degree with KU Leuven, Leuven, Belgium. His current research interests include MIMO radars, biomedical signal processing, electromagnetic propagation, wireless power transfer, and RF and microwave systems design.
PROF. DOMINIQUE SCHREURS (Fellow, IEEE) received the M.Sc. degree in electronic engineering and a Ph.D. degree from the University of Leuven, Leuven, Belgium, in 1992 and 1997, respectively. She is currently a Full Professor with KU Leuven, Leuven, and the Chair of LICT, Leuven. She has been a Visiting Scientist with Agilent Technologies, Santa Rosa, CA, USA; ETH Switzerland; and the National Institute of Standards and Technology, Boulder, CO, USA. Her current research interests include microwave and millimeter wave characterization and modeling of transistors, nonlinear circuits and bioliquids, as well as system design for wireless communications and biomedical applications. Prof. Schreurs served as the President of the IEEE Microwave Theory and Techniques Society (April 2018–2019) and was priorly the Editor-in-Chief of IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES as well as IEEE MTT-S Distinguished Microwave Lecturer. She also served as a President of the ARFTG organization (2018–2019) and was the General Chair of the 2007, 2012 and 2018 Spring ARFTG Conferences.
PROF. PETER KARSMAKERS received his Ph.D. at KU Leuven, Department of Electrical Engineering, in 2010. In 2013 as a post-doc, he co-founded the ADVISE research group at Geel campus together with a few other colleagues. From 2018, he was an Assistant Professor at KU Leuven in the Computer Science Department, and since 2022, he has been a principal investigator at Flanders Make. His research focuses on designing machine learning algorithms that consider application-specific constraints. These can, for example, relate to the computing platform on which the algorithm will be deployed or background knowledge about the machine learning task.
