Mohammad Mamouei
Machine Learning Scientist
Mohammad is a Machine Learning Scientist at the University of Oxford. As a member of the interdisciplinary research group, Deep Medicine led by Prof Kazem Rahimi, Mo specialises in the applications of machine learning on large-scale electronic health records to extract new insights and develop models for prognostication and diagnostication. He is involved in the PEAK Urban and Informal Cities programmes where he focuses on the effects of environmental and sociodemographic factors on health outcomes.
Before joining the University of Oxford, he was a postdoctoral researcher at City, University of London focusing on the analysis of high-dimensional optical spectra and bio-signals, and a research assistant at University of Southampton focusing on urban mobility.
Mo's educational background is in Applied Mathematics-Systems and Modelling (PhD, City, University of London), and Electrical Engineering (MSc, City, University of London & BSc, Iran University of Science and Technology).
He also leads a team of data scientists at a charity, Apart of Me, that has embarked upon the ambitious project of delivering the first mobile game to help children cope with bereavement.
Recent publications
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Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records.
Journal article
Rao S. et al, (2024), IEEE Trans Neural Netw Learn Syst, 35, 5027 - 5038
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An empirical investigation of deviations from the Beer-Lambert law in optical estimation of lactate.
Journal article
Mamouei M. et al, (2021), Sci Rep, 11
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Design and Analysis of a Continuous and Non-Invasive Multi-Wavelength Optical Sensor for Measurement of Dermal Water Content.
Journal article
Mamouei M. et al, (2021), Sensors (Basel), 21