Mr. Aymane Edder | Ai and Iot in Healthcare | Best Researcher Award

Edder Aymane is a highly motivated third-year PhD student specializing in Tiny Machine Learning (TinyML) for vital signs monitoring. Based in Casablanca, Morocco, his research focuses on developing efficient machine learning models deployable on embedded systems to analyze and interpret biomedical data in real-time, ultimately enhancing health monitoring and diagnostics. His technical expertise spans TinyML, embedded systems, IoT, biomedical signal processing, model optimization, and programming languages such as Python, C/C++, and MATLAB. Edder has gained hands-on experience through internships at ERAMEDIC, CHU Ibn Rochd, MEDICINA, and BRET LAB, contributing to various healthcare technology projects including the development of remote monitoring prototypes for COVID-19 patients. His academic journey reflects a strong foundation in biomedical engineering, industrial science physics, and medical analysis, complemented by extensive practical skills. Fluent in Arabic and English, with intermediate proficiency in French, Edder Aymane is committed to advancing real-time healthcare solutions through innovative machine learning applications.

Profile

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🎓 Education

Edder Aymane’s educational path demonstrates a strong interdisciplinary foundation. He is currently pursuing his PhD at UM6SS, Casablanca (2023–present), focusing on TinyML applications for healthcare monitoring. He earned his Engineering Degree in Biomedical Sciences from ENSAM Rabat (2019–2022), where he worked on practical projects such as remote vital parameters monitoring for COVID-19 patients. Prior to that, he completed preparatory classes in Industrial Science Physics (PSI) (2017–2019), which equipped him with a strong base in physics and engineering principles. Throughout his academic training, Edder engaged in various internships at leading healthcare and research institutions, including ERAMEDIC, CHU Ibn Rochd, and MEDICINA, where he gained real-world experience in laboratory analysis, medical device installation, and healthcare informatics. His academic career combines theoretical learning with hands-on practice, positioning him well for advanced research in biomedical machine learning and embedded systems.

🧪 Experience

Edder Aymane has developed extensive professional experience through diverse internships and research projects related to healthcare technology and biomedical engineering. At ERAMEDIC (July–Sept 2021), he contributed to the installation of Neuro-Navigation Surgery Software and pre-installation of radiology rooms. During his time at MEDICINA (July–Aug 2020), he performed internal and external quality control of biochemistry, hematology, and serology automat systems. At CHU Ibn Rochd, he was involved in setting up COVID-19 services and developing remote monitoring prototypes for vital signs during the pandemic. His end-of-studies internship at BRET LAB (2023–present) further strengthened his research expertise in biomedical signal processing and TinyML model development. These experiences allowed him to apply his technical skills in embedded systems, IoT integration, and machine learning, giving him a well-rounded profile in both research and applied biomedical technologies.

🏅 Awards and Honors

While specific formal awards and honors are not listed, Edder Aymane’s consistent selection for highly technical internships and research projects at reputable healthcare institutions demonstrates recognition of his expertise and potential. His involvement in cutting-edge projects such as the development of a prototype remote vital parameters monitor for COVID-19, installation of complex neuro-navigation systems, and leadership roles during his internships indicate a high level of trust from supervisors and collaborators. His acceptance into the PhD program at UM6SS to work on emerging fields like TinyML reflects both academic and professional acknowledgment of his abilities. Additionally, his multidisciplinary skillset in machine learning, embedded systems, and biomedical signal processing showcases his outstanding technical competency, positioning him as a promising researcher poised for future honors as his academic career progresses.

🔬 Research Focus

Edder Aymane’s research focuses on leveraging Tiny Machine Learning (TinyML) for real-time health monitoring and diagnostics. His work involves developing highly efficient machine learning models optimized for deployment on embedded systems with limited computational resources. Specifically, he focuses on analyzing biomedical signals such as ECG data, enabling continuous monitoring of vital signs directly from wearable or portable devices. His research integrates advanced signal processing techniques, noise filtering, IoT protocols (MQTT, CoAP, BLE), and real-time data interpretation, contributing to more accessible, scalable healthcare solutions. By combining biomedical engineering with embedded AI, Edder aims to bridge the gap between sophisticated machine learning models and practical, low-power medical devices. His work has significant implications for early diagnostics, remote patient monitoring, and scalable healthcare delivery, particularly in resource-limited settings. This research contributes to the growing field of personalized, preventive healthcare powered by intelligent, real-time monitoring systems.

✅ Conclusion