AI Research Engineering

The CMI's AI Research Engineering (AIRE) team carry out leading-edge AI and machine learning research, working collaboratively across disciplines to develop standardized, open-source software solutions with potentially high impact.

On this page:

The AIRE Mission:

Working collaboratively with the University research community AIRE will:

  • assess and select proposals for AI/ML projects, analysing the requirements, feasibility, and impact

  • conduct leading-edge AI/ML research to address gaps and challenges

  • design, build, and deploy software libraries following FAIR principles (findability, accessibility, interoperability, and reusability)

  • disseminate research outcomes via high quality research publications, tutorial, hackathons etc.

Working with Special Interest Groups

The AIRE prioritizes interdisciplinary research projects which will result in potentially high impact.   The AIRE invites proposals via the Shef.AI community special interest groups: 

  • to encourage community engagement and interdisciplinary collaboration

  • to help researchers to find multiple partners from across disciplines

  • to help manage the process of project proposal and selection 

  • to provide a "joined-up" approach to software development

To receive information on future AIRE project proposal calls and to be eligible to participate in AIRE projects .


CMI and Research Software Engineering (RSE)

The CMI is also offering AI support for researchers across the university facilitated by the CMI-RSE team. This service will be more suitable for smaller scale, shorter, projects that are likely to involve less fundamental AI research.  

Find out more about working with the CMI and the RSE

AIRE Current Projects:

Brain imaging


Harnessing advanced AI technology to discern novel biomarkers, paving the way for enhanced chronic nerve pain treatments and revolutionising healthcare outcomes.

Prasun Tripathi, Dinesh Selvarajah, Jim Wild, Haiping Lu, Shuo Zhou, Solomon Tesfaye, David Bennett, Andrew Segerdahl, Leslie Colvin, Douglas Steele.

University of 91Ö±²¥: Faculty of Medicine, Dentistry and Health, Faculty of Engineering.
External Partners: University of Oxford, University of Dundee, AstraZeneca.

Historical data

Leveraging deep learning to refine OCR transcriptions of the extensive British Library Newspapers collection to overcome the barrier of inaccurate text data, unveiling a rich resource for exploring centuries of historical narratives and advancing global humanities research.

Alan Thomas, Robert Gaizauskas, Valeria Vitale, Michael Pidd, Robert Shoemaker, Haiping Lu.

University of 91Ö±²¥: Faculty of Arts & Humanities, Faculty of Engineering, Digital Humanities Institute (DHI).
External Partner: British Library.

Cardiothoracic image

Utilising advanced AI to process multimodal cardiothoracic data for enhanced diagnosis and prognosis of Cardiothoracic Disease (CTD), paving the way for personalised medical care and transformative approaches in heart and lung health.

Mohammod Suvon, Andrew Swift, Venet Osmani, Samer Alabed, Pete Metherall, Shuo Zhou, Haiping Lu.

University of 91Ö±²¥: Faculty of Medicine, Dentistry and Health, Faculty of Social Science, and Faculty of Engineering.
External Partner: 91Ö±²¥ Teaching Hospitals NHS Foundation Trust.

AIRE Project Horse

Exploring innovative methods to optimize AI models using multi-fidelity data, aiming to enhance accuracy and efficiency across engineering disciplines by leveraging diverse data sources to improve model performance while reducing reliance on high-cost precision data.

Haolin Wang, Wei Xing, Donghwan Shin, Jose L. Casamayor, Haiping Lu

University of 91Ö±²¥ Collaborating Faculties: Faculty of Science, Faculty of Engineering and AMRC

Digital Materials Discovery

Using digital databases and advanced AI to predict new, sustainable materials for green technologies, accelerating the shift towards net-zero emissions and driving substantial progress in global sustainability initiatives.

Xianyuan Liu, Joshua Berry, Kathy Christofidou, Nicola Morley, Haiping Lu

University of 91Ö±²¥ Collaborating Faculties: Faculty of Engineering, Royce Institute

Multimodal AI for Parkinson's Disease

Utilizing advanced artificial intelligence to process multimodal data to enhance the diagnosis and prognosis of Parkinson’s Disease, paving the way to uncover the underlying mechanisms of Parkinson’s Disease and improve its prediction.

Wenrui Fan, Thomas W. Payne, Mohammod Suvon, Xianyuan Liu, Mahjabin Islam, Haolin Wang, Jiayang Zhang, Venet Osmani, Chen Chen, Shuo Zhou, Oliver Bandmann, Haiping Lu

University of 91Ö±²¥ Collaborating Faculties: 91Ö±²¥ Institute for Translational Neuroscience (SITraN) and Faculty of Engineering

Donor Acknowledgement

The AIRE team recently received a donation from alumni medics, Dr Dinesh and Dr Sandra Naik.

This generous gift will assist in our current research into developing AI models for predicting treatment responses and clinical phenotypes in neuropathic pain, as well as advancing general multimodal AI approaches for similar challenges. This work is important for addressing neuropathic pain, a significant health issue caused by damage or disease affecting the nervous system. Our efforts aim to fill crucial gaps and address challenges in this area, making a tangible difference in both the field of AI research and patient care.

We are most grateful to the donors for their support of our mission to conduct leading AI/machine learning research. Their backing not only propels our current initiatives but also highlights the collaborative spirit essential for tackling complex health conditions like neuropathic pain.