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Current research areas include bioinformatics, computer architecture, computer vision, distributed systems, graphics and human-computer interaction, logic and semantics, machine learning, natural language processing, networking and wireless communication, operating systems and virtualization, programming, security, and sustainable computing

The Computer Laboratory undertakes research in a broad range of subjects within the disciplines of Computer Science, Engineering, Technology, and Mathematics. Current research areas include bioinformatics, computer architecture, computer vision, distributed systems, graphics and human-computer interaction, logic and semantics, machine learning, natural language processing, networking and wireless communication, operating systems and virtualization, programming, security, and sustainable computing.

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Recent Submissions

  • International comparison of bank fraud reimbursement: Customer perceptions and contractual terms 

    Becker, Ingolf; Hutchings, Alice; Abu-Salma, Ruba; Anderson, Ross John; Bohm, Nicholas; Murdoch, Steven James; Sasse, Angela et al. (Oxford University Press, 2018-06-01)
    We set out to investigate how customers comprehend bank terms and conditions (T&Cs). If T&Cs are incomprehensible, then it is unreasonable to expect customers to comply with them. An expert analysis of 30 bank contracts ...
  • HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology. 

    Shrestha, Raunak; Hodzic, Ermin; Sauerwald, Thomas Michael; Dao, Phuong; Wang, Kendric; Yeung, Jake; Anderson, Shawn et al. (2017-09)
  • Interviewing cybercrime offenders 

    Hutchings, Alice; Holt, Thomas J
    Research on cybercrime offending and victimization has increased dramatically over the past two decades, though qualitative scholarship on more technical offenses such as computer misuse have not kept pace with this broader ...
  • Dictionary Learning Inspired Deep Network for Scene Recognition. 

    Liu, Yang; Chen, Qingchao; Chen, Wei; Wassell, Ian James (2018)
    Scene recognition remains one of the most challenging prob- lems in image understanding. With the help of fully con- nected layers (FCL) and rectified linear units (ReLu), deep networks can extract the moderately sparse ...

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