Department of Computer Science and Technology

Artificial Intelligence Group

Members of the AI research group after formal dinner at Magdelene College, Cambridge.

Academic staff

  • Prof. John Daugman OBE FREng
    Computer vision, statistical pattern recognition, information theory, wavelets, and chess algorithms.
  • Dr Sean Holden
    Machine learning algorithms, computational learning theory, Bayesian inference, application of machine learning in theorem proving and organelle proteomics.
  • Prof. Mateja Jamnik
    Computational modeling of human reasoning. Artificial intelligence, human-like computation, automated reasoning, machine learning (explainability, personalised medicine), diagrammatic reasoning, knowledge representation, cognitive science, tutoring systems in education.
  • 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 molecules-cell-tissue-organ 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.

Post-doctoral 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
    Human-like computing, logic-based knowledge representation and automated reasoning, argumentation theory, intelligent agents and multi-agent 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 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.

Research students

  • Edward Ayers (Prof. M. Jamnik; Prof. Timothy Gowers, DPMMS)
    Automated Mathematician
  • Tiago Azevedo (Prof. P. Liò, Prof. M. Spillantini)
    Machine Learning and Multi-scale Modelling of Tauopathies
  • Cristian Bodnar (Prof. P. Liò)
    Evolution guided learning
  • 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 cross-modal scenarios
  • Maxwell Conway (Prof. P. Liò)
    Computational Modeling of Comorbidities
  • 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
  • Giovanna Maria Dimitri (Prof. P. Liò)
    Development of network methodologies for brain data modelling
  • 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
  • Felix Opolka (Prof. P. Liò)
    Attention, Conditioning and interpretability in Deep Learning
  • 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 Spivakovsky-Gonzalez (Prof. P. Liò)
    Cold Fish
  • Aaron Stockdill (Prof. M. Jamnik)
    Automating representation change across domains for reasoning
  • Petar Veličković (Prof. P. Liò)
    Overcoming challenging data scenarios in deep learning
  • 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
  • Hui Xiao (Prof. P. Liò; Prof. Anne Ferguson-Smith, MoleBio)
    Multi-omic data integration in mammalian early embryonic development and human diseases
  • 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, end-user 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, human-robot 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, content-based 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, large-scale graph processing, big data, graph database, parallel data-flow programming, data driven declarative networking, delay tolerant networks, bio-inspired networks and social networks, complex and time-dependent networks, wireless sensor networks, mobile peer-to-peer systems, data synchronisation, caching, and replication, event-based distributed systems, event correlation.
For individual contact details, see also the Laboratory’s list of all members.