Ahmad Bazzi was born in Abu Dhabi, United Arab Emirates. He received his PhD degree in electrical engineering from EURECOM, Sophia Antipolis, France, in 2017, and the MSc degree (summa cum laude) in wireless communication systems (SAR) from Centrale Supélec, in 2014. He is currently a Research Scientist at the Wireless Research Lab of New York University (NYU) Abu Dhabi, and NYU WIRELESS, NYU Tandon School of Engineering, contributing to integrated sensing and communications (ISAC). Prior to that, he was the Algorithm and Signal Processing Team Leader at CEVA-DSP, Sophia Antipolis, leading the work on Wi-Fi (802.11ax) and Bluetooth (5.xx BR/BLE/BTDM/LR) high-performant (HP) PHY modems, OFDMA MAC schedulers, and RF-related issues. He is an inventor with multiple patents involving intellectual property of Wi-Fi and Bluetooth products, all of which have been implemented and sold to key clients. He is a Senior Member of the IEEE. He is a full member of Sigma Xi (ΣΞ), The Scientific Research Honor Society. Since 2018, he has been publishing lectures on the YouTube platform under his name “Ahmad Bazzi”, where his channel contains mathematical, algorithmic, and programming topics, with over 270,000 subscribers and more than 17 million views, as of November 2024. He was awarded a CIFRE Scholarship from Association Nationale Recherche Technologies (ANRT) France, in 2014, in collaboration with RivieraWaves (now CEVA-DSP). He was nominated for Best Student Paper Award at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2016. He received the Silver Plate Creator Award from YouTube, in 2022, for his 100,000 subscriber milestone. He was awarded an exemplary reviewer for the IEEE Transactions on Communications (TCOM) in 2022, an exemplary reviewer for the IEEE Wireless Communications Letters (WCL) in 2022, and an exemplary reviewer for the IEEE Open Journal of the Communications Society (OJ-COMS) in 2025. He served as technical program committee (TPC) member and a reviewer for many leading international conferences. He was selected amongst top 200 Top Arab creators for 2023. He serves as an editor of the IEEE Communications Letters (COMML), 2025-2026, an editor for the IEEE OJ-COMS, 2025-2026. He also serves as a guest editor for the IEEE OJ-COMS special issue on “Resilient and Trustworthy Communications for 6G Wireless Environments: Integrating Sensing, AI, and Security in Smart Wireless Systems”, 2026. His research interests include signal processing, wireless communications, artificial intelligence, statistics, and optimization.
Lyudmila Mihaylova is Professor of Signal Processing and Control in the School of Electrical and Electronic Engineering at the University of Sheffield, Sheffield, United Kingdom.
Her research interests are in the areas of trustworthy autonomous multi-agent systems with applications to smart cities, sensor networks, and others. She has expertise in the areas of machine learning, intelligent sensing and sensor data fusion.
Prof. Mihaylova has published more than 200 scientific papers in peer reviewed international journals such as IEEE Transactions on Aerospace and Electronic Systems, IEEE Transactions on Signal Processing, Automatica, IEEE Transactions on Industrial informatics and in a number of conferences.
She is Associate Editor-in-Chief for the IEEE Transactions on Aerospace and Electronic Systems, Senior Editor for the Target Tracking and Multi-sensor data Fusion area since 2021, and a Subject Area Editor for the Elsevier Signal Processing Journal since 2022. She is a guest Editor for a special issue for Frontiers of Robotics and AI (2022-2023).
She is a member of the organising committee of the International Conference of Information Fusion 2025, 2022, 2021, IEEE MFI’ 2021, UKCI’ 2021 and vice-chair of the UKCI 2022. She was the general vice-chair for the International Conference on Information Fusion 2018 (Cambridge, UK), of the IET Data Fusion & Target Tracking 2014 and 2012 Conferences, publications chair for ICASSP 2019 (Brighton, UK) and others.
Speech Title
Bridging the Gap between the AI, Machine Learning and Intelligent Systems