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Mohammed S. Elmusrati Full Professor of Telecommunication and Smart Systems
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Abstract: The principle of uncertainty lies at the heart of all scientific disciplines. Across physics, engineering, biology, and beyond, theories and research efforts are ultimately driven by a common goal: to uncover truth by reducing uncertainty.
Uncertainty is most naturally and rigorously expressed through the lens of probability and statistics. In this talk, we will begin by introducing the nature of uncertainty itself and briefly review traditional methods used to quantify and manage it.
However, our primary focus will be on how modern machine learning and artificial intelligence can address the problem of uncertainty in ways that were previously unimaginable. We will outline the fundamental concepts of AI and machine learning, and explore how these technologies are reshaping our approach to data, prediction, and decision-making.
As a practical highlight, we will take a brief and dynamic tour through real-world applications of AI and machine learning in wireless communication systems, with a particular emphasis on their role in current and future cellular networks—including 5G and beyond.
Bio:
Prof. Mohammed S. Elmusrati received his B.Sc. (with honors) and M.Sc. (with high honors) degrees in Telecommunicmodelinggineering from the Department of Electrical and Electronic Engineering at Benghazi University (formerly Garyounis University), Libya, in 1991 and 1995, respectively. He earned his Licentiate of Science (with distinction) and Doctor of Science degrees in Control Engineering from Aalto University (formerly Helsinki University of Technology), Finland, in 2002 and 2004.
Currently, Prof. Elmusrati is a Full Professor at the University of Vaasa, where he leads the Cyber-Physical Systems (CPS) research group and serves as Head of the Sustainable Autonomous Systems program. He has supervised over 140 master’s theses and 12 doctoral dissertations, all successfully completed.
Prof. Elmusrati has co-authored more than 185 peer-reviewed journal articles, conference papers, books, and technical reports. His research interests include machine learning, mobile and wireless networks, stochastic systems, uncertainty modeling, and smart grids.
