Profile page of Dr Mohammed Mamouei
“Multi-parametric optical sensing for monitoring septic shock” (Current, EPSRC-funded project).
“The impacts of user-optimal and system-optimal driving approaches for autonomous vehicles in large urban networks”, supported by University of Southampton and Transport Systems Catapult Research Centre.
“Efficient Control of Autonomous vehicles”, funded by City, University of London and World Cities World Class University (WC2) Network.
Dr Mohammad Mamouei is a post-doctoral researcher in Machine Learning at the Research Centre for Biomedical Engineering (RCBE). His main areas of expertise are machine learning, optimisation and signal processing.
Mohammad obtained his bachelor’s degree in electrical engineering in 2011 at University of Science and Technology, Tehran, Iran. He then moved to London to continue his studies in Electrical Engineering at City, University of London. In 2013, he obtained a master’s degree in Telecommunications and Networks. He was then awarded a full studentship to do a PhD in Applied Mathematics-Systems and Modelling, with a focus on modelling and control of autonomous vehicles. During his PhD, due to his interest in the commercialisation of new technologies, Mohammad was also an active member of London City Incubator, a unit of the Research and Enterprise office of City, University of London that provides commercialisation consultancy for City academics and researchers, and other consultancy services such as feasibility study, market research and strategic business modelling for City spin-out companies.
Following the completion of his PhD, Mohammad joined the RCBE in January 2018 where he is currently working on an EPSRC-funded project that aims to uncover the complex interrelationships between noninvasive, optically-obtained biosignals and septic shock. This is a multidisciplinary project carried out by a large group of researchers and collaborators with a diverse range of expertise, including machine learning, signal processing, computer science, electrical engineering, optical spectroscopy, and biochemistry. The team is envisaged to design and develop a new, non-invasive optical sensor and leverage machine learning techniques to provide continuous monitoring of lactate in blood. In addition, the research will rigorously investigate and acquire new understanding of the relationship between tissue oxygenation and blood lactate levels in extreme conditions involving short term episodes of hyperlactatemia, in order to improve the monitoring and prediction of life-threatening shock.