Dr Mohamed Elbadawi
Lecturer in Computational Physiology / Biomedicine (T&R)
Email: m.elbadawi@qmul.ac.ukRoom Number: Room 4.13, Fogg buildingWebsite: https://www.linkedin.com/in/dr-moe-elbadawi/Support Hours: Please email to arrange a meeting
Profile
Bio
Dr Mohamed Elbadawi is the module lead for ‘AI and Data Analytics in Physiology and Biomedicine’. Dr Elbadawi’s research centres on the use of digital technologies to advance healthcare, including machine learning, robotics and 3D printing.
He has been named on the ‘Stanford Top 2% Scientist’ for two consecutive years (2022, 2023).
Academic Degrees
PhD Mechanical Engineering, University of Sheffield 2014-2017
MSc Biomedical Engineering, University of Surrey 2012-2013
BSc Pharmacology, University of Bristol 2007-2010
Available Research Opportunities
PhD
An Innovative Machine Learning Pipeline For Accelerating the Translation of Medicinal Hemp
Postgraduate Teaching
Module Lead
BIO729P: AI and Data Analytics in Physiology and Biomedicine
Bio730P: Developing AI solutions in the Biosciences
For this module, PG students have the opportunity to exercise what they have learnt to develop innovative solutions for unmet biosciences needed. Students work in teams to plan, implement and present their solutions for a given topic. Examples of research topic include solutions in drug discovery, medical image analysis and healthcare Internet of Things (IoT).
Research
Research Interests:
- Artificial Intelligence for Drug Discovery & Drug Development
- 3D Printing for manufacturing precision medicine
- Bioelectronics for programmable drug delivery systems
- Rheology for characterising viscoelastic materials
Publications
Public Engagement
AI for Managing Medicines
Working with TSIP, we hosted workshops with the South London Community to co-produce AI solutions for helping patients to independently manage their own medication.
Scholarly Contributions
Editorial Roles
Editorial Board member of Frontiers in Industrial Microbiology