Artificial Intelligence Group
Academic staff
 Prof. John Daugman OBE FREng
Computer vision, statistical pattern recognition, information theory, wavelets, and chess algorithms.  Dr Carl Henrik Ek
Probabilistic models, approximate inference.  Dr Sean Holden
Machine learning algorithms, computational learning theory, Bayesian inference, application of machine learning in theorem proving and organelle proteomics.  Dr Ferenc Huszár
Approximate inference, active learning, and applications of machine learning in sciences.  Prof. Mateja Jamnik
Computational modeling of human reasoning. Artificial intelligence, humanlike computation, automated reasoning, machine learning (explainability, personalised medicine), diagrammatic reasoning, knowledge representation, cognitive science, tutoring systems in education.  Dr Nicholas Lane
Machine learning and computational systems.  Prof. Neil Lawrence
Machine learning.  Prof. Pietro Liò
Machine learning and computational models in health Big Data Predictive models in Personalised medicine Methods for combining Multi scale, Multi omics and Multi physics modelling of moleculescelltissueorgan interactions Developing and testing methodologies for modeling biomedical systems Super meta: Meta Analysis and Omics integration bioinformatics  Dr Thomas Sauerwald
Randomised algorithms (in particular for load balancing or information dissemination), markov chains and random walks, distributed computing, graph theory, game theory.
Postdoctoral researchers

Dr Helena Andres Terre
Cell decision making, integration of structural, genetic and epigenetic data  Dr Daniel Raggi
The role of representation in reasoning. The relation between formal systems and human cognition. Modeling mathematical reasoning. Understanding 'understanding'.  Dr Nicolas Rivera
 Dr Zohreh Shams
Humanlike computing, logicbased knowledge representation and automated reasoning, argumentation theory, intelligent agents and multiagent systems.  Dr Nikola Simidjievski
Machine Learning and data mining algorithms, algorithms for data integration and fusion, application of machine learning and data mining in systems medicine and systems neuroscience, comutational scientific discovery, knowledge representation, mining and modeling complex systems and networks.  Dr Gem Stapleton
Diagrammatic logics, spider diagrams, concept diagrams, and information visualisation.  Dr John Sylvester
Markov chains and random processes on graphs. Random graphs. Randomised and distributed algorithms.  Dr Luca Zanetti
Spectral graph theory (with applications to algorithm design and machine learning). Randomised and distributed algorithms. Markov chains.
DECAF fellows

Dr Bingqing Cheng
Machine learning and computational physics. 
Dr Bianca Dumitrascu
Machine learning and genetics. 
Dr Challenger Mishra
Machine learning and Theoretical Physics. 
Dr Sarah Morgan
Data science for mental health.
Research students

Edward Ayers (Prof. M. Jamnik; Prof. Timothy Gowers, DPMMS)
Automated Mathematician 
Tiago Azevedo (Prof. P. Liò, Prof. M. Spillantini)
Machine Learning and Multiscale Modelling of Tauopathies 
Pietro Barbiero (Prof. P. Liò)
Towards Interpretable Artificial Intelligence 
Cristian Bodnar (Prof. P. Liò)
Evolution guided learning 
David Buterez (Prof. P. Liò)
Unsupervised learning for modelling single cell to organ data 
Leran Cai (Dr T. Sauerwald)
Network algorithms based on Markov Chains 
Alexander Campbell (Prof. P. Liò)
To be agreed 
Cătălina Cangea (Prof. P. Liò)
Machine learning for crossmodal scenarios 
Benjamin Day (Prof. P. Liò)
Developing AI inspired by statistical physics 
Jacob Deasy (Prof. P. Liò)
Machine learning in emergency care 
Botty Dimanov (Prof. M. Jamnik)
Interpretable deep learning 
Dobrik Georgiev (Prof. P. Liò)
Neural execution of graph algorithms 
Paris Flood (Prof. P. Liò)
Machine Learning for personalized healthcare 
Dmitry Kazhdan (Prof. M. Jamnik)
Learning the next generation of drug targets by modelling diseases, targets and their relationships 
Dimitrios Los (Dr T. Sauerwald)
Applications of supervised machine learning algorithms to NLP 
Chaitanya Mangla (Dr S. B. Holden, Prof. L. Paulson)
Machine Learning for Automated Theorem Proving 
Andrei Margeloiu (Prof. M. Jamnik)
Towards Reliable Deep Learning Systems in Medicine 
Urška Matjašec (Prof. M. Jamnik)
Making deep neural networks more transparent by explaining their decisions 
Eric Meissner (Prof. N. D. Lawrence)
AutoAI via Meta Modelling in Machine Learning Systems 
Jacob Moss (Prof. P. Liò)
Machine learning for systems biology 
Felix Opolka (Prof. P. Liò)
Attention, Conditioning and interpretability in Deep Learning 
Andrei Paleyes (Prof. N. D. Lawrence)
Frameworks for Surrogate Modelling and Emulation 
Emma Rocheteau (Prof. P. Liò, Dr R. Cardinal)
Predicting outcomes in psychiatric disorders using reinforcement learning 
Hayk Saribekyan (Dr T. Sauerwald)
Information spreading in distributed computing 
Paul Scherer (Prof. P. Liò, Prof. M. Jamnik)
Machine Learning on Graph Structured Data for Oncology 
Agnieszka Słowik (Prof. M. Jamnik, Dr S. B. Holden)
Machine learning for logical reasoning 
Simeon Spasov (Prof. P. Liò)
Modelling metabolic and communication dysfunctions in Parkinson's Diseases 
Pablo SpivakovskyGonzalez (Prof. P. Liò)
Cold Fish 
Aaron Stockdill (Prof. M. Jamnik)
Automating representation change across domains for reasoning 
Ramon Viñas Torné (Prof. P. Liò)
Generating realistic Multiomic data 
Duo Wang (Prof. M. Jamnik, Prof. P. Liò)
Bridging Computer Science with Neuroscience towards a new understanding of reasoning 
Junwei Yang (Prof. P. Liò)
Deep learning for neuroscience and back 
Jin Zhu (Prof. P. Liò)
A Clinical Decision Support System for Cerebral Vascular Diseases
Associated academic researchers
 Alan Blackwell
Visual representation, enduser development, interdisciplinary design, tangible augmented and embodied interaction, psychology of programming, computer music, critical theory.  Ted Briscoe
Computational linguistics, speech and language processing, textual information management, evolutionary linguistics.  Anne Copestake
Natural language processing (NLP) / computational linguistics, representation issues, compositional and lexical semantics, natural language generation.  Richard Gibbens
Mathematical modelling of networks especially communication networks, road transport networks, energy networks.  Hatice Gunes
Artificial emotional intelligence, affective computing, personality computing, social signal processing, human behaviour understanding, social robotics, humanrobot interaction, intelligent user interfaces, human sensing in virtual reality, assistive technologies.  Marwa Mahmoud
Automating machine understanding of emotional body language, including expressions of emotions or medical conditions.  Cecilia Mascolo
Mobile and sensor systems, mobility modelling, mobile applications, mobile data analysis.  Simone Teufel
Text understanding  Chris Town
Computer vision, contentbased image retrieval and search, optical character recognition (OCR) and biological pattern recognition.  Damon Wischik
Mathematics and machine learning, dashboards for taxis cars and trains, incentives.  Eiko Yoneki
Data centric systems and networking, largescale graph processing, big data, graph database, parallel dataflow programming, data driven declarative networking, delay tolerant networks, bioinspired networks and social networks, complex and timedependent networks, wireless sensor networks, mobile peertopeer systems, data synchronisation, caching, and replication, eventbased distributed systems, event correlation.