Mohamed E. Dwedar is a PhD candidate at the Technical University of Munich (TUM), specializing in Cyber-Physical Systems, Artificial Intelligence, and connected multi-agent robotics. His research focuses on designing intelligent, scalable, and trustworthy decision-making systems for real-time environments, where multiple robots, drones, and sensors collaborate through IoT and advanced communication frameworks such as MQTT and 5G. He is particularly interested in reducing bias in distributed decision-making, enabling robust coordination between heterogeneous agents, and ensuring reliable system behavior under uncertainty.

Alongside his academic work, Mohamed is a Microsoft Cloud Solution Architect with extensive experience in Azure IoT, AI, and DevOps solutions. He has led and supported enterprise-scale implementations involving secure device connectivity, real-time data pipelines, and cloud-based machine learning deployment. His expertise bridges the gap between theoretical research and industrial application, allowing him to design end-to-end systems that integrate edge devices, cloud infrastructure, and intelligent models.

His technical background spans robotics, machine learning, and embedded systems, including work with LiDAR, radar, and vision-based perception systems, as well as simulation environments using ROS and MATLAB. He actively develops solutions based on deep reinforcement learning, vision-language models, and sensor fusion to enable autonomous navigation, hazard detection, and adaptive control in dynamic environments.

Mohamed has contributed to multiple research and industry projects, including AI-driven laser cleaning systems and 5G-based anomaly detection for machine-to-machine communication. His work has been published internationally, and he continues to focus on advancing the state of the art in AI-enabled cyber-physical systems, with an emphasis on real-time deployment, safety, and scalability.