Dr Mohamed Elbadawi

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

See Mohamed's Google Scholar Page

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