Mohammad Eissa

Dr. Mohammad Eissa is a Lecturer in Digital Electronics at the School of Electrical and Electronic Engineering, University of Sheffield. He holds a PhD in translational digital engineering for chronic conditions and has held postdoctoral roles focusing on machine learning, AI, and biomarkers. He is an honorary researcher at Sheffield Teaching Hospitals, analyzing diabetes care and the impact of the pandemic on diabetes patients. His research interests include machine learning, digital healthcare technology, IoT, SoC, and hardware design. Collaborations with the Royal College of Arts and involvement in the DAFNEplus trial highlight his focus on inclusive healthcare solutions. He teaches courses such as EEE125, EEE232, and EEE6225.

Education:

• Bachelor's in Computer Engineering (University of Sheffield)
• MSc in Data Communication Engineering (University of Sheffield)
• PhD in Translational Digital Engineering

Research Interests: Machine Learning/AI, Digital Healthcare Technology, IoT, System-on-Chip Design, and Hardware Implementation. His work emphasizes practical applications in healthcare, including diabetes management and wearable technologies.

Key Collaborations: Royal College of Arts (design-tech integration), Sheffield Teaching Hospitals (clinical diabetes research). He actively seeks industrial partnerships to advance healthcare tech and data-driven solutions.

Teaching: Undergraduate and postgraduate courses in electrical and electronic engineering, including EEE125, EEE232, and EEE6225.