Teams

Led by Professor Ruth Ahnert, Professor of Literary History and Digital Humanities in the School of English and Drama, the lab will seek to leverage the potential of historical archives using digital approaches. Ahnert is especially interested in expanding work on the development of historical infrastructures and communications using methods from network science. Future work will build on her recent project based at The Alan Turing Institute, Living with Machines, and seek to expand UK capacity in digital humanities through the development of historical datasets, tools, and communities of practice. 

Led by Professor Mike Barnes, the group has broad computational biology research interests spanning the translational research and drug discovery continuum, from genetic and genomic methods for target identification to clinical informatics, patient stratification and multiple long-term conditions.

The Bessant Lab is a multidisciplinary group dedicated to the development of machine learning, AI and data integration methods for application to complex biomedical datasets. We have a particularly strong track record of studies involving large scale proteomic mass spectrometry, which can reveal important markers of cellular dynamics on a proteome-wide level. Such markers include alternative splicing, novel gene products, protein activity and protein conformation.

While our previous work has made extensive use of machine learning, we are increasingly turning to logic-based approaches to turn heterogeneous data into intuitive biochemical models that can be used to automatically explain experimental observations and generate novel biological hypotheses. The Bessant Lab is led by Professor Conrad Bessant.

The Duffy lab, led by Dr Chris Duffy, are interested in energy relaxation in molecular systems. In other words, following some excitation by an external force, how does the system return to its original state. A practical example of this is the biological light-harvesting proteins, natural solar devices that are evolved to capture, transfer and transform light energy with ultra-efficiency. By combining quantum theory, non-linear spectroscopy and data techniques we try to understand how these processes are controlled and could possibly be replicated.

The Game AI research group, led by Professor Simon Lucas, uses games as a test-bed for the application of advanced artificial intelligence (AI) methods. They study two main approaches to general game AI; deep learning, and deep statistical search using a forward planning model (including Monte Carlo Tree Search and Rolling Horizon Evolution), and use AI methods for procedural content generation in games and other creative media. The group is also associated with the EPSRC-funded IGGI Centre for Doctoral Training, a leading PhD research programme aimed at the Games and Creative Industries.

Professor Cédric John leads the John Lab which focuses on the application of machine learning and AI in Earth and Planetary Sciences. The lab's work in computer vision enables the analysis of unstructured visual data from geological cores and satellite imagery of Earth and other planets. Addressing the Small Dataset challenge, the lab prioritizes data quality and robustness. It also employs Generative AI for the analysis of geological images, satellite and seismic data. The lab's interdisciplinary research contributes to the fields of Earth Science, environmental change, and clean energy.

Led by Professor David Leslie, the current research focuses on digital ethics, algorithmic accountability, explainability, and the social and ethical impacts of machine learning and data-driven innovations. In particular, he is keen to question how the biospherically and geohistorically ramifyingscope of contemporary scientific innovation (in areas ranging from AI and synthetic biology to nanotechnology and geoengineering) is putting pressure on the conventional action-orienting categories and norms by which humans, at present, regulate their behaviour.

Led by Professor Gina Neff, this research group works at the intersection of social science and human-centred AI with a focus on the social, cultural, economic and political impacts of emerging technologies. We have a particular focus on work and organisations and on data and AI systems in everyday life. Current projects include work on youth and generative AI with Dr Jaimie Freeman and Dr Claudine Tinsman, work and automation with Professor Carrie Sturts Dossick,  and cultural attitudes on AI with Dr Peter Nagy and Dr Clementine Collette.   

Led by Professor Venet Osmani, the lab focuses on transforming healthcare through machine learning research. By analysing large-scale patient data, including medical records, biomarkers, imaging, multi-omics, and wearable device data, the lab develops methods to optimise treatment strategies, enhance patient outcomes, and address health inequities. The team possesses expertise in theoretical machine learning, including generative AI methods for synthetic patient data, algorithmic bias, model fairness and explainable AI. The lab collaborates with the leading clinical and research institutions worldwide to translate research into clinical practice.

Led by Dr Caroline Roney the group specialises in developing models of personalised physiology to simulate different treatment approaches. An example application is developing engineering methodologies to personalise treatment approaches for cardiac arrhythmias. They use a combination of signal processing, machine learning and computational modelling techniques to develop novel methodologies for investigating cardiac arrhythmia mechanisms from clinical imaging data and electrical recordings. They aim to translate the tools we develop for analysing electrical and imaging data to clinically predict optimal patient specific treatment strategies

Led by Professor Greg Slabaugh, Professor of Computer Vision and AI at Queen Mary and Director of DERI, the lab focusses on computer vision and deep learning with applications to computational photography and medical image computing.

Led by Dr Shanxin Yuan, the research focuses on computer vision and machine learning, with a strong philosophy of transferring research to significant real-world applications. They work on several lines of research, especially 3D digital humans and computational photography. The recent topics include hand/head/body pose estimation and reconstruction, neural rendering for deformable objects, pose retargeting, immersive gaming, music understanding, and fashion AI. Dr Yuan’s previous research has been successfully shipped to several products that are being used by millions of people worldwide.