Mohammed JOUHARI

Leads advanced engineering courses and directs impactful research projects in cybersecurity and network virtualization, contributing significantly to student development and curriculum innovation.

• Delivered comprehensive instruction in Unix systems, application security, and virtualization to hundreds of engineering students, enhancing their foundational and advanced technical skills.
• Supervised over 15 final year projects, guiding students from conception to successful completion in areas like secure automation, network management, and full-stack development.
• Developed and updated 5+ core course programs, integrating new pedagogical approaches to improve learning outcomes and align with industry demands in areas like SI design and governance.
• Contributed to the academic community as a technical committee member for 7+ international conferences, including IEEE GLOBECOM and WINCOM, fostering research dissemination.
• Served as a peer reviewer for prestigious IEEE journals, including IEEE Access, IoT Journal, and Wireless Communications Letters, ensuring high-quality research contributions.

Conducted cutting-edge research on machine learning applications for IoT network energy efficiency, contributing to international collaborations and high-impact publications.

• Developed and applied machine learning models to optimize energy efficiency in IoT networks, achieving significant advancements in sustainable communication protocols.
• Authored and co-authored high-impact research papers, including a survey on Scalable LoRaWAN (IEEE Communications Surveys & Tutorials, IF: 33.84) and anomaly detection in Industrial IoT (Sensors, IF: 3.847).
• Collaborated with international research teams, fostering cross-cultural scientific exchange and expanding research horizons in advanced networking.
• Presented research findings at multiple international conferences, including IEEE GLOBECOM 2024 and ICC 2023, showcasing expertise to a global audience.
• Explored innovative applications of Deep Reinforcement Learning for Flying LoRa networks, enhancing performance and autonomy in dynamic environments.

Pioneered research in machine learning methods for drone surveillance, securing project funding and mentoring master's students, resulting in significant publications.

• Designed and implemented advanced machine learning methods for drone-based surveillance systems, enhancing efficiency and accuracy in monitoring operations.
• Coordinated and managed externally funded research projects, ensuring timely delivery and adherence to scientific objectives and budget constraints.
• Published impactful research, including a paper on Distributed CNN on UAVs (IEEE IoT Journal, IF: 9.936), significantly contributing to the field of unmanned aerial vehicle technology.
• Mentored and supervised master's students, guiding their research and academic development in advanced computing topics such as AI and network optimization.
• Contributed to the development of robust Intrusion Detection Systems for IoT environments, enhancing network security and data integrity.

Delivered specialized courses in data security, VoIP, and IPv6, while conducting impactful research on underwater wireless sensor networks and supervising master's theses.

• Instructed university-level courses on critical topics such as data security, Voice over IP (VoIP), and IPv6, preparing over 200 students for evolving network challenges.
• Developed and optimized algorithms for underwater wireless sensor networks, contributing to advancements in challenging communication environments.
• Supervised multiple master's thesis projects, providing mentorship and academic guidance to students pursuing advanced studies in networking and security.
• Authored a comprehensive survey on Underwater Wireless Sensor Networks (IEEE Access), establishing expertise in a niche research area.
• Contributed to the academic community by developing novel solutions for network security and communication protocols.

Guided and mentored over 15 engineering students through their final year projects at EMSI, covering diverse technical domains and ensuring practical application of theoretical knowledge.

Comprehensive survey on scalable LoRaWAN architectures, contributing to advancements in low-power wide-area networks (Impact Factor: 33.84).

Research on anomaly detection techniques for industrial IoT environments, enhancing security and operational integrity (Impact Factor: 3.847).

Developed a robust Intrusion Detection System using Deep Reinforcement Learning, significantly improving threat detection capabilities (Impact Factor: 5.978).

Explored distributed Convolutional Neural Networks on Unmanned Aerial Vehicles, optimizing on-board processing for real-time applications (Impact Factor: 9.936).

Comprehensive survey on Underwater Wireless Sensor Networks, addressing challenges in MAC layer, topology control, and routing protocols.