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Research

Our people

DERI facilitates interdisciplinary collaborations between Queen Mary’s Faculties of Science and Engineering, Humanities and Social Sciences and the School of Medicine and Dentistry in the areas of digital and data science.

The Institute directly supports around 150 staff located within the building, as well as creating wider research networks across the University.

Our research teams

Our current research teams include:

Game AI Research Group

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.

The Bessant Lab

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

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 Slabaugh Lab

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.

The Decision-Support Lab

The decision-support lab is led by Dr William Marsh and is a part of the Risk and Information Management (RIM) research group in EECS. We aim to develop practical techniques for decision-support with probabilistic and causal models (primarily using Bayesian networks). Much of the work is collaborative with application to medicine and in engineering. As well as developing probabilistic models for new applications, our current work includes: 

  • Techniques for efficient model building from knowledge and for group elicitation.
  • Reusable interfaces so that probabilistic predictors become usable decision-support systems.
  • Techniques for reasoning about interventions and counterfactual.
  • Algorithms for explaining predictions and presenting the evidence supporting a model.
  • Empirical evaluation of the effectiveness of decision-support systems in practice.