Computer Science @ University of St Andrews

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Research

  • Academics in robes with Vint Cerf
  • Lecture on web technologies
  • First year lecture
  • MSc students in the lab
  • MSc students in the lab
  • Undergraduate tutorial
  • Lecture
  • MSc students enjoying a sunny day
  • MSc students at the summer BBQ
  • First year students in a lab-based exercise class
  • MSc students in the snow after November graduation
  • MSc students after graduation
  • Undergraduate students after summer graduation
  • Blue sky thinking
  • Lecture
  • The Jack Cole building
  • Staff discussion

We are a top-class group of researchers with interests in a wide range of areas of theoretical and practical computer science. All our academic staff are research active, working with a team of post-graduate and post-doctoral researchers and a lively population of research students. See our directory of staff and research students.

Our research focuses on core themes of theoretical and practical computer science:

  • Artificial intelligence and symbolic computation including constraint programming, computational algebra and computational logic and natural language processing, image processing and robotics.
  • Computer systems engineering including software architecture cloud computing and next-generation internet, programming models for distributed systems, pervasive systems and data linkage analysis.
  • Human Computer Interaction including pervasive and ubiquitous computing, input and output technologies, intelligent interactive systems and visualisation.
  • Programming languages with an emphasis on type systems and parallelism in functional programming languages.

The School is a member of SICSA - The Scottish Informatics and Computer Science Alliance.

We have research support from a range of funding bodies including the Engineering and Physical Sciences Research Council (EPSRC), the European Commission and Industry.

All School publications are published via the University of St Andrews Research Portal.

Search Publications from the School of Computer Science


See also: completed Computer Science PhD theses.

Recent Publications

Discovering topic structures of a temporally evolving document corpus

Finding parallel functional pearls: automatic parallel recursion scheme detection in Haskell functions via anti-unification

Computer-aided parameter selection for resistance exercise using machine vision-based capability profile estimation

Follicle Stimulating Hormone is an accurate predictor of azoospermia in childhood cancer survivors

Kelsey, TW, McConville, L, Edgar, AB, Ungurianu, AI, Mitchell, RT, Anderson, RA & Wallace, WHB 2017, 'Follicle Stimulating Hormone is an accurate predictor of azoospermia in childhood cancer survivors' PLoS One, vol 12, no. 7, e0181377. DOI: 10.1371/journal.pone.0181377

Correct Composition of Dephased Behavioural Models

Bowles, JKF & Caminati, MB 2017, Correct Composition of Dephased Behavioural Models. in 14th International Conference on Formal Aspects of Component Software. LNCS, Springer.

MapMySmoke: feasibility of a new quit cigarette smoking mobile phone application using integrated geo-positioning technology, and motivational messaging within a primary care setting

Automatically improving constraint models in Savile Row

Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk

Li, J & Arandelovic, O 2017, Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk. in 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society. pp. 4199.

Towards objective and reproducible study of patient-doctor interaction: automatic text analysis based VR-CoDES annotation of consultation transcripts

Birkett, C, Arandelovic, O & Humphris, GM 2017, Towards objective and reproducible study of patient-doctor interaction: automatic text analysis based VR-CoDES annotation of consultation transcripts. in 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society. pp. 2638.

Diagnosis prediction from electronic health records (EHR) using the binary diagnosis history vector representation