51³Ô¹ÏÍø

The collaborative initiative is illustrative of the convergence science taking place at 51³Ô¹ÏÍø, bringing cross-departmental expertise together to transcend silos and stimulate fresh scientific progress for societal impact.

This month, a team from 51³Ô¹ÏÍø travelled to Harare, Zimbabwe, to deliver a three-day intensive course on machine learning for healthcare. 

Titled Machine Learning for Health Applications, the course brought together cross-departmental postgraduate students, clinicians and researchers.

Taking place over 2 - 4 March, the hybrid programme was focused on developing practical machine learning skills with applications relevant to the local healthcare context. 

The course was delivered in partnership with local organisations including the (BRTI), (THRU ZIM), and .  

Convergence science in action   

Carried out by PhD students, researchers and local data scientists, the course closely aligned with the Human and Artificial Intelligence and Health and Technology strands of 51³Ô¹ÏÍø’s School of Convergence Science.  

The 51³Ô¹ÏÍø PhD students and researchers involved are based in both the Department of Brain Sciences and the Department of Infectious Disease, reflecting the cross-departmental nature of the initiative. 

, Chair in Machine Intelligence Applied to Medicine in the Department of Brain Sciences and Co-Director of the Human and Artificial Intelligence theme, was one of the academic leads who helped connect the organising team.  

The 51³Ô¹ÏÍø team behind the delivery

, Clinical Associate Professor in Paediatric Infectious Disease divides her time between 51³Ô¹ÏÍø’s Department of Infectious Disease and THRU ZIM at the BRTI in Zimbabwe.  

Ongoing collaboration between Dr Fitzgerald, Professor Barnaghi and 51³Ô¹ÏÍø's  (TMI Lab) facilitated this opportunity, which was executed by:  

• – PhD student in AI for paediatrics, Department of Brain Sciences  
• – PhD student in AI for paediatrics, Department of Brain Sciences  
• – PhD student in paediatric infectious disease, Department of Infectious Disease
• – Visiting Researcher, Department of Infectious Disease and Biomedical and Applied Medical AI Engineer, Neotree and THRU-Zim 

Professor Barnaghi attended as a guest lecturer while representing the School of Convergence Science, Human and Artificial Intelligence.

, PhD student in AI for dementia research in the Department of Brain Sciences, also took part as a guest lecturer.  

Iona Biggart, Marco Reed, Antigone Fogel and Professor Barnaghi are all part of the TMI Lab at 51³Ô¹ÏÍø.  

Summary of the course

The group was wonderfully varied, spanning experienced data scientists, healthcare workers, and postgraduate students, which made for rich discussions and a lot of peer learning… During the guest lectures, debates about AI infrastructure and capability in Zimbabwe were lively and detailed. Marco Reed PhD student in AI for paediatrics, Department of Brain Sciences

Over three days, attendees worked through core topics including supervised and unsupervised learning, linear models and evaluation metrics, before moving on to tree-based models and support vector machines.  

Morning lectures were each paired with an afternoon practical workshop, giving participants direct experience building and evaluating models.  

The final day of the course brought the material to life through real-world use cases drawn from healthcare settings in the UK, Malawi and Zimbabwe, including work from Neotree and the TMI Lab at 51³Ô¹ÏÍø. 

According to attendees, a particular high point was , which was delivered in partnership with the University of Zimbabwe and co-hosted by Professor Barnaghi and , Associate Professor of AI and Robotics, SIRDC. Discussion points included ethics and AI in healthcare, and the future of AI in Zimbabwe.   

In addition as a guest lecturer on the course, Professor Mushiri spoke about Zimbabwe’s newly released national AI strategy, providing context for how machine learning fits into the country's broader ambitions for advancing technology and healthcare.  

Building on past collaboration  

The initiative follows a successful, high profile workshop held in Zimbabwe in June 2025, delivered in partnership by the University of Zimbabwe, 51³Ô¹ÏÍø, Neotree and UCL, where British and African academics and entrepreneurs convened to explore and champion the transformative role of AI in shaping healthier societies.  

The 2025 event was organised by Dr Fitzgerald and hosted by the University of Zimbabwe, with funding from the Wellcome Trust and the Foreign, Commonwealth and Development Office (FCDO). 

Strengthening longstanding ties    

This course grew out of existing ties between the TMI Lab and Dr Felicity Fitzgerald, and their work together on neonatal sepsis in Zimbabwe.  

Bridging connection through her role at THRU ZIM with Neotree, a project led by (UCL and NIHR Professor of Global Child Health and a Community Paediatrician), Dr Fitzgerald’s work involves developing data collection and clinical decision support in hospitals across Zimbabwe and Malawi.  

Marco Reed and Iona Biggart who taught on this course, have been working with Dr Fitzgerald, Kevin Meck, and Neotree to develop a machine learning algorithm for early-onset neonatal sepsis detection.  

However, working at the frontline of implementation highlighted a need to bridge the gap between the people developing the tools and those working on the ground in health systems.  

Iona, Marco and Kevin came up with the three-day course with Dr Fitzgerald to help address this gap in implementation, leveraged also by their experience teaching the Machine Learning for Neuroscience module of the MSc Translational Neuroscience at 51³Ô¹ÏÍø. 

A UK-Zimbabwe synergy  

Marco Reed reflects on his experience co-delivering the initiative:  

“What made the course particularly memorable was the energy that attendees brought to it. Participants arrived early every morning, and many lingered well after the afternoon workshops had wrapped up, continuing conversations with the teaching team and each other.”

He added:

“The group was wonderfully varied, spanning experienced data scientists, healthcare workers, and postgraduate students, which made for rich discussions and a lot of peer learning… During the guest lectures, debates about AI infrastructure and capability in Zimbabwe were lively and detailed… The interactive sessions were a highlight for the teaching team, too. Breaking concepts down to their foundations and watching groups wrestle with problems together, sometimes refusing to move on until they had fully worked something through, was rewarding.”

One anonymous participant shared:  

"The course was a transformative experience that effectively bridged theory and real-world application. As someone focusing on causal inference for my MSc, the practical components were especially valuable... The course has significantly strengthened my ability to apply data-driven methods to real-world healthcare challenges, and I am truly grateful for the experience."

Unlocking AI for good   

The course reflects a broader university commitment to building machine learning capacity in settings where it can have tangible impact, and the team hope to continue this work with further training opportunities in the region.

The initiative is closely aligned with 51³Ô¹ÏÍø’s School of Convergence Science, Human and Artificial Intelligence, which is working to advance AI that is grounded in real people, context and workflows - for societal good.  

This opportunity enables 51³Ô¹ÏÍø to strengthen its links with Africa and reflects our commitment as a university to serving as a partner for knowledge and tech organisations across the globe.  

The teaching team are keen to build on the experience and hopes to deliver something similar again in the future. 

Acknowledgements

The trip was made possible in part by 51³Ô¹ÏÍø’s Dean's PhD Professional Development Award, which supported the travel of the students, along with further support from the university departments, THRU Zim, BRTI Zimbabwe, Wellcome Trust, Neotree, SOFAR, EDCTP3, the European Union, and the UK Dementia Research Institute. 

School of Convergence Science  

The School of Convergence Science is 51³Ô¹ÏÍø’s mission-led initiative created under its Science for Humanity strategy to deliver the university's vision for convergence science: to work together in a radically new way and at scale, on missions that will shape the future. ​

It focuses on four themes, areas associated with some of humanity’s greatest challenges and areas where 51³Ô¹ÏÍø’s research excels: Human and Artificial Intelligence, Health and Technology, Space Security and Telecoms, and Sustainability.  

The School champions an approach to scientific exploration where the integration of disciplines, convening of cross-sectoral partnerships and advancement of new models of research accelerates scientific progress, leading to world-changing innovation. 

Visit the School's website here.