معرفی
Luc Berthouze is a Professor of Complex Systems (Informatics) at the School of Engineering and Informatics, University of Sussex, where he leads interdisciplinary research at the intersection of mathematics, engineering, and neurophysiology. He also holds an honorary appointment at the Great Ormond Street Hospital Institute of Child Health, University College London.
His educational background includes a PhD in Applied Mathematics and Computer Science from the University of Evry Val d’Essonne (1996), followed by research positions at the Electrotechnical Laboratory (ETL) and the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, before joining the University of Sussex faculty in 2006.
His research focuses on dynamical processes on networks, including neuronal synchronization, epidemic spreading, and fault detection in IT infrastructures. He develops mathematical models and analysis methods rooted in dynamical systems, random processes, graph theory, control theory, and signal processing. Applications span neuroscience (EEG, EMG, MEG), robotics, and large-scale network management.
The trend in his recent publications reveals a strong emphasis on network dynamics across biological and technological systems. Key themes include functional connectivity inference, metastability in oscillatory networks, epidemic modeling on structured networks, and scalable observability in microservices. His work increasingly bridges neuroscience and computer engineering, exemplified by projects like 'Rethinking large-scale network management through the lens of neuroscience.'
- Mathematical Neuroscience
- Mathematical Epidemiology
- Time Series Analysis
- Network Analysis
- Synchronization and Criticality
- Motor Control and Coordination
He has received substantial research funding from EPSRC, Innovate UK, Moogsoft, Aviva, and the Leverhulme Trust, supporting work on network controllability, data-driven decision making, and Bayesian inference. His grants often involve collaborations with industry partners, indicating applied and translational impact.
He supervises research projects and teaches courses such as 'Intelligence in Animals and Machines,' contributing to both undergraduate and postgraduate education. His lab and research team work on developing scalable mathematical frameworks for network analysis, with applications in healthcare, robotics, and IT infrastructure.
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