Mohammad Al-Sad

I am a multidisciplinary scientist with a background in electrical engineering, signal processing, neuroscience, machine learning, and education. I have received my B.Sc. and M.Sc. degrees in electrical engineering from Qatar University, Doha, Qatar, in 2012 and 2016, respectively, and the Ph.D. degree in electrical engineering and computing sciences from Tampere University, Tampere, Finland, in 2022, and my professional teaching qualification from Häme University of Applied Sciences, Hämeenlinna, Finland, in 2025. Currently, I am a postdoctoral research fellow in signal processing at the Department of Neuroscience at the University of Helsinki, the BABA center at the Helsinki University Hospital, and the Department of Computing Sciences at Tampere University. 

My research interests include time-frequency signal theory, machine learning, neuroscience, electroencephalogram analysis and processing, information flow and theory, and signal modeling and optimization. I am an editorial board member in the Science Journal of Circuits, Systems and Signal Processing and a technical reviewer for several journals, including IEEE Transactions on Signal Processing, IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Artificial Intelligence, Digital Signal Processing, Signal Processing, Biomedical Signal Processing and Control, and IEEE Access.

My research focuses on advancing signal processing and machine learning for dynamic, high-precision systems in medical and industrial domains. Over 14 years at the University of Helsinki, Helsinki University Hospital, Tampere University, and Qatar University, I have gained expertise in time-frequency analysis, machine learning, multi-sensor signal processing, IoT systems, and neuroscience. This multidisciplinary focus bridges practical applications with theoretical advances, creating impactful solutions to complex challenges.

Non-stationary signal analysis, machine learning, electroencephalogram analysis, functional brain networks, signal modelling, optimization, digital signal processing, applied probability and statistics, industrial and medical engineering applications, and science education.