Mohammed Hindawi

Mohammed Hindawi is a Teacher-Researcher at CESI in Villeurbanne, France, affiliated with the Engineering and Digital Tools research team. His academic foundation includes a Doctorate (2013) and Master of Research (2008) in Computer Science from INSA Lyon, with specializations in knowledge systems, alongside Master's (2006) and Engineering (2005) degrees in Software Engineering from the University of Aleppo, Syria.

Research Focus: His work spans machine learning methodologies like dimension reduction and variable selection, applied to digital health and battery technology. Primary domains include:

• Semi-supervised feature selection algorithms
• Multimodal sensor data analysis for healthcare
• AI-driven battery state estimation
• Frugal AI solutions for resource-constrained environments

Educational Activities: Hindawi teaches computer science across CESI's engineering cycle, covering embedded systems, object-oriented programming, databases, AI, and advanced algorithms. He designs curriculum modules and tutors preparatory/engineering students.

Research Leadership: As a member of the 'Frugal and Embedded AI' working group, he explores efficient machine learning implementations. He currently supervises PhD candidate Trésor YAO KOFFI's thesis on multimodal machine learning for patient monitoring systems.

Publication Trends: Hindawi's recent publications (2023-2024) demonstrate a shift toward applied AI in healthcare and energy systems, building on his foundational work in semi-supervised feature selection (2011-2016). His research consistently addresses real-world constraints through innovative ML architectures.