Mohamed BAKHOUYA is a professor of computer science at the International University of Rabat, Morocco. He obtained his HDR from UHA-France in 2013 and his PhD from UTBM-France in 2005. He has more than 10 years experiences in participating and working in sponsored projects. He was PI of Aalto starting grant at Aalto University-Finland (2011-2013), Co-PI (UTBM side) of two European projects ASSET (Advanced Safety and Driver Support in Efficient Road Transport, FP7-SST, 2008-2011, and TELEFOT (Field Operational Tests of Aftermarket and Nomadic Devises in Vehicles, FP7-ICT, 2008-2012. He spent two years as a research scientist in USA at the George Washington University, HPC laboratory participating and working in sponsored projects, mainly UPC (Unified Parallel C) and NSF Center of High-performance and Reconfigurable Computing. He was also a member (UTBM side) of EU EACEA Erasmus Mundus project TARGET I/II (Transfer of Appropriate Requirements for Global Education and Technology), 2011-2015. He was PI of CASANET project (CNRST, 2016-2019), Co-PI of SELFSERV (VLIR-UOS, 2016-2018), Co-PI of AFRIKATATERRE (Solar Dechatlon AFRICA, 2018-2019), Co-PI of MIGRID (USAID-PEER program, 2017-2020), and PI of HELECAR (PSA OpenLAB@Maroc, 2017-2020). Hi is currently PI of HOLSYS project (IRESEN, 2020-2022). He was a reviewer of research project for Agence Nationale de la Recherche, (France, 2011), Ministero dell' Istruzione, dell' Università e della Ricerca (Italy, 2012, 2013, 2016, 2017), Qatar National Research Fund (2019, 2020) and for European Commission-FP7 (2013-2015). He was EiC of IJARAS journal and also serves as a guest editor of a number of international journals, ACM Trans. on Autonomous and Adaptive Systems, Product Development Journal, Concurrency and Computation: Practice and Experience, FGCS, and MICRO. He has published more than 100 papers in international journals, books, and conferences. His research interests include various aspects related to the design and implementation of distributed and adaptive systems using Big data, CEP, and predictive control techniques with applications in energy efficient buildings and electromobility.