Prof. Mohsen Guizani, IEEE Fellow

Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE

Mohsen Guizani (Fellow, IEEE) received the BS (with distinction), MS and PhD degrees in Electrical and Computer engineering from Syracuse University, Syracuse, NY, USA in 1985, 1987 and 1990, respectively. He is currently a Professor of Machine Learning at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. Previously, he worked in different institutions in the USA. His research interests include applied machine learning and artificial intelligence, smart city, Internet of Things (IoT), intelligent autonomous systems, and cybersecurity. He became an IEEE Fellow in 2009 and was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2019, 2020, 2021 and 2022. Dr. Guizani has won several research awards including the “2015 IEEE Communications Society Best Survey Paper Award”, the Best ComSoc Journal Paper Award in 2021 as well 5 Best Paper Awards from ICC and Globecom Conferences. He is the author of 11 books, more than 1000 publications and several US patents. He is also the recipient of the 2017 IEEE Communications Society Wireless Technical Committee (WTC) Recognition Award, the 2018 AdHoc Technical Committee Recognition Award, and the 2019 IEEE Communications and Information Security Technical Recognition (CISTC) Award. He served as the Editor-in-Chief of IEEE Network and is currently serving on the Editorial Boards of many IEEE Transactions and Magazines. He was the Chair of the IEEE Communications Society Wireless Technical Committee and the Chair of the TAOS Technical Committee. He served as the IEEE Computer Society Distinguished Speaker and is currently the IEEE ComSoc Distinguished Lecturer.

Speech Title: "Intelligent Edge Computing for Smart City Applications"

Abstract: Artificial Intelligence (AI), the Internet of Things (IoT) and advanced wireless communications are transforming our society by connecting the world and making it more intelligent. Future smart cities will focus on improving the quality of life by enabling various applications, such as autonomous driving, extended reality, brain-computer interaction, and healthcare. These applications will have diverse performance requirements (e.g., user-defined quality of experience metrics, latency, and reliability) which will be challenging to be fulfilled by existing wireless systems. To meet the diverse requirements of the emerging applications, the concept of smart IoT has been recently proposed and used. An IoT using computing technologies (e.g., edge computing), security related technologies (e.g., blockchain) and machine learning, to enable smart city applications. On the other hand, federated learning (FL) has provided a private platform in many of these applications to protect the data and reduce latency. These smart services/applications rely on efficient computation and communication resources. Furthermore, being able to provide adequate services using these complex systems presents enormous challenges. In this talk, we present an overview of the use of AI and IoT in smart cities. Then, we showcase our research activities that will contribute to these efforts and advocate possible solutions using these models. We provide ways on how to manage the available resources intelligently and efficiently to offer better living conditions for our citizens and provide better services. Finally, we discuss some of our research results and future directions to support a variety of applications.

Prof. Yew-Soon Ong, IEEE Fellow

Nanyang Technological University, Singapore

Yew-Soon Ong (Fellow of IEEE) received the Ph.D. degree in artificial intelligence in complex design from the University of Southampton, U.K., in 2003. He is President’s Chair Professor in Computer Science at Nanyang Technological University (NTU), and is the Chief Artificial Intelligence Scientist of the Agency for Science, Technology and Research in Singapore. At NTU, he also serves as Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab, and Director of the Data Science and Artificial Intelligence Research Center. He was Chair of the School of Computer Science and Engineering at NTU from 2016-2018. His research interest is in artificial and computational intelligence, presently in Machine Learning, Evolution Computation and Optimization. He is Editor-in-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence and AE of IEEE TNNLS, the IEEE Cybernetics, IEEE TEVC, IEEE TAI and others. He has received several IEEE outstanding paper awards, Nanyang Education Excellence Award and was listed as a Thomson Reuters highly cited researcher and among the World's Most Influential Scientific Minds.

Speech Title: "Deriving Set of Model Sets from Large Pretrained Models with Neuroevolutionary Multitasking"

Abstract: Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These large pre-trained models or “Jacks of All Trades” (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions, or varied task requirements, could limit the real-world utility of a singular JAT. Hence, in tandem with current trends towards building increasingly large JATs, this talk presents an exploration into concepts underlying the creation of a diverse set of compact machine learning model sets. Composed of many smaller and specialized models, the Set of Sets is formulated to simultaneously fulfil many task settings and environmental conditions. A means to arrive at such a set tractably in one pass of a neuroevolutionary multitasking algorithm is presented for the first time, bringing us closer to models that are collectively “Masters of All Trades”.