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Professor Moamar Sayed-Mouchaweh

Computer Science

Institut Mines-Télécom Lille Douai

Dr Moamar Sayed-Mouchaweh received his Master's degree from the University of Technology of Compiègne, France, in 1999, before obtaining his PhD degree from the University of Reims, France, in 2002. He worked as an Associate Professor in Computer Science, Control and Signal Processing at the University of Reims' Research Center in Information and Communication Science and Technology (CReSTIC), before, in 2008, he obtained the Habilitation to Direct Research (HDR) in Computer Science, Control and Signal Processing. Since 2011, he has been working as a Full Professor at the Institut Mines-Télécom (IMT) Lille Douai, France.

He has edited several books with Springer, such as Learning in Non-Stationary Environments: Methods and Applications, Fault Diagnosis of Hybrid Dynamic and Complex Systems, Diagnosability, Security and Safety of Cyber-Physical Systems and Learning from Data Streams in Evolving Environments, and also wrote two SpringerBriefs books in Electrical and Computer Engineering: Discrete Event Systems: Diagnosis and Diagnosability, and Learning from Data Streams in Dynamic Environments.

Moamar has been a guest editor of several special issues of international journals. He was the Chair and IPC Chair of several national and international conferences (IEEE International Conference on Machine Learning and Applications, IEEE International Conference on Evolving and Adaptive Intelligent Systems, etc.). He is currently a member of the editorial board of the Elsevier journal Applied Soft Computing and the Springer journals Evolving systems and Intelligent Industrial Systems.

The aim of his research activities is to develop advanced techniques and tools allowing generating models that are able to efficiently predict the behavior of a wide range of real applications. The goal of these models is to optimize the system performance or to significantly improve its safety, reliability, availability and to reduce its exploitation and maintenance costs.

Some of the major applications of the developed models are: robust and early fault diagnosis in wind energy turbines in order to increase their availability and reliability and to reduce their maintenance costs; intrusion detection and misuse of information; decision aid making for crisis management; improving the safety of critical systems such as nuclear reactors; early diagnosis of prostate cancer and follow-up of its evolution in response to medical treatment; energy management (load forecasting, balancing and optimizing generation and consumption) in smart grid operation; smart metering in the presence of renewable energy; and smart home management (energy optimization and activity recognition) and its integration into the electrical grids.