Mohanad Alkhodari

Alkhodari’s research interests include machine/deep learning, biosignals and bioimaging, and healthcare informatics. His current research focuses on developing artificial intelligence tools to leverage personalised healthcare in clinical practice, particularly for cardiovascular assessment. The ultimate goal of his DPhil project is to understand the hypertension progression landscape over the life course with the help of evolutionary AI-based models and large-scale multi-organ multi-modality data. 

His research has led to the development of novel methodologies for estimating severity and discovering distinct phenotypes based on multi-organ damage associated with hypertension, offering new insights into disease mechanisms. As part of his DPhil, Alkhodari designed and developed HyTwin, an AI-assisted prototype software that integrates his research algorithms to enable efficient clinical implementation. Alongside the team, Alkhodari's research has received international recognition, including being ranked among the top 25 student-led studies at the 2023 IEEE BIBM conference. His HyTwin prototype was further honoured with recognition on the 2024 Forbes 30 Under 30 MENA list and named a semi-finalist for the 2024 MIT Technology Review Innovators Under 35 Global after receiving the award for the MENA region in 2023.

With an h-index/i10-index of 15/23, Alkhodari authored and co-authored three book chapters and more than 50 scientific papers in international journals and conferences, where he was the first, leading, corresponding, or presenting author in majority of them. Alkhodari is an associate editor at PLoS ONE and an active reviewer for several reputable journals including IEEE JBHI and Frontiers in Physiology.