Fethi Filali

Fethi Filali has had a diverse and accomplished career in the field of technology and research. Starting in 1999, they worked as a Ph.D student at INRIA before moving on to become an Assistant Professor at the University of Nice Sophia-Antipolis in 2002. In 2003, Filali joined EURECOM as an Associate Professor, where they taught graduate courses and led research projects until 2010. During this time, they also served as a Senior Research Scientist at Qatar Mobility Innovations Center (QMIC) from 2010 to 2011. Filali then took on various leadership roles at QMIC, including Head of Technology Development & Applied Research, Senior Manager of Technology Development & Planning, and Director of Technology & Research (CTO). Additionally, they have been an Adjunct Full Professor at Hamad Bin Khalifa University since 2018. Throughout their career, Filali has demonstrated expertise in a range of areas, including wireless networking, multimedia protocols, mobile applications and services, vehicular networks, and sensor networks.

Fethi Filali's education history includes a Habilitation degree in Computer Science from Université Côte d'Azur (2002-2008), a Ph.D. in Computer Science from Université Côte d'Azur (1999-2002), a Master of Research degree in Networking and Distributed Systems from ENSI - Ecole Nationale des Sciences de l'Informatique (1998-1999), and an Engineer's degree in Computer Science from ENSI - Ecole Nationale des Sciences de l'Informatique (1996-1998).

In addition to their formal education, Fethi Filali has also obtained several certifications, including the Machine Learning Engineering for Production (MLOps) Specialization, Deploying Machine Learning Models in Production, Introduction to Machine Learning in Production, Machine Learning Data Lifecycle in Production, Machine Learning Modeling Pipelines in Production, Convolutional Neural Networks, Machine Learning, Sequence Models, Deep Learning Specialization, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Structuring Machine Learning Projects, Neural Networks and Deep Learning, and React Native: Advanced Concepts.