MBZUAI Adjunct Professor
Mérouane Debbah
topped off 2021 by celebrating his inclusion on Clarivate’s 2021 Highly Cited Researchers List. The Highly Cited Researchers List is a global trademarked thought leadership platform that has ranked Debbah among the top one percent of researchers worldwide.
“We are proud of this global recognition for Professor Mérouane Debbah,” MBZUAI President, Professor Eric Xing, said. “He joined the department of machine learning in 2021 and is already having a big impact for the university.”
This accolade is on top of a host of previous recognitions including more than 20 best paper awards. Debbah is also a distinguished journal editor and industry speaker. His research interests lie in fundamental mathematics, algorithms, statistics, information, and communication sciences research. Currently, he is working on multi-agent reinforcement learning, distributed or persuasive AI, and exascale models.
“This has a huge impact, and it is a way of recognizing the work I’ve been doing,” Debbah said.
AI and telecommunications
Debbah is an elite researcher in AI and telecommunications systems and is the chief researcher at the
Technology Innovation Institute (TII)
in Abu Dhabi. He has worked for some of the world’s leading telecommunications providers and research centers including Motorola Labs (France), the Vienna Research Centre for Telecommunications, Institut Eurecome (France), and Huawei (France). He has managed eight EU projects and more than 24 national and international projects, and he hopes to work with UAE national telecommunications provider Etisalat in the near future.
“AI has a lot of potential to improve end-to-end quality of transmission,” Debbah said. “Today, the improvements for networks are slow because we have a hard time understanding end-to-end quality. We understand the subparts of the network but not all together.”
Debbah is part of a team of wireless researchers investigating a new kind of device that passively reflects radio waves called a reconfigurable intelligent surface (RIS). The technology has shown promise in the reduction of wireless dead spots.
“The second thing we want to look at for telecom is energy consumption. Improving the energy efficiency of networks is extremely complicated because we don’t have a good idea of the models, on how things are working and how, for example, a base station or a server consumes energy,” Debbah said. “In order to conduct this research, we need to come up with policies for shutting down parts of the network.”
Algorithms and drones
Drone light shows are amazing. But what goes into putting on such spectacular shows? Debbah is interested in this area known as multi-agent reinforcement learning.
“This is basically how a swarm of drones can interact seamlessly without any kind of accident,” he said. “For multi-agent reinforcement learning you need to build up algorithms to tell the system how to behave. The big problem of these algorithms is that we cannot ensure that the system can work for any kind of configuration.”
“When you use these algorithms in real life, you want to be sure that they’re robust. Robust means that you have done the experiments for any given configuration. For example, with four entities it works but does it work with five, six or seven?”
Fellowships and awards
Debbah’s recognition as a highly cited researcher is one in a long line of academic and professional accolades. He has been name a fellow by IEEE, WWRF, Eurasip, AAIA and the Institut Louis Bachelier. He is also the winner of the 2018 and 2021 IEEE Marconi Paper Prizes, the 2017 and 2021 Eurasip Best Paper Awards, the 2020 SEE Blondel Medal, the 2019 IEEE Radio Communications Committee Technical Recognition Award, and the 2019 IEEE Communications Society Young Author Best Paper.
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Mohamed bin Zayed
University of Artificial Intelligence
Masdar City, Abu Dhabi
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