Özgür Şimşek

Professor Özgür Şimşek

Özgür’s research is on artificial intelligence and machine learning, with emphasis on reinforcement learning. She is particularly interested in open-ended learning in complex, dynamic, uncertain environments. Areas of interest include: 1) Statistical properties of natural environments that enable fast, effective learning, 2) Autonomous construction of hierarchies of reusable skills, 3) Intrinsic motivation and curiosity.


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Dr Julian Padget

Julian’s main research focus is the use of formal approaches to validation and verification with computational logic-based models. Application domains include legal reasoning, checking and monitoring security policies, investigating interactions between policies and their automatic revision (inductive logic programming), gaming, virtual and mixed environments and agent-based modelling.


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Professor Eamonn O’Neill

Eamonn’s research has the overarching goal of developing an applied science of human-computer interaction (HCI). This involves developing a sound theoretical footing for HCI and deriving design principles for the development of human-computer systems that are theoretically well-founded, empirically tested and operationalised for people’s use.


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Dr Tom Fincham Haines

Tom applies machine learning (ML) to a wide selection of problems, particularly those involving computer visions and graphics. He has strong interests in graphical models, Bayesian non-parametric models, directional statistics and active learning, and working on projects involving tools to help artists, ML for education, online/realtime ML, causality, and scaling non-parametric methods to big data.


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Professor James Davenport

James’s main research interest is computer algebra, especially symbolic integration, simplification and equation solving. One specific application has been using computer algebra to generate numerical code. He has side-interests in efficient parallelism, electronic mathematical publishing and “mathematics on the (semantic) Web”, robot motion planning and cryptography, especially cracking US public-key cryptosystems.


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Professor Neill Campbell

Neill’s main area of research involves learning models of shape (2D and 3D) and appearance from images. In particular, he is interested in performing this in an automatic or interactive fashion that allows these technologies to be put to use in a variety of applications without requiring users to have computer vision or graphics expertise. He also works on generative machine learning models, in particular Gaussian processes and Bayesian nonparametric methods, in a variety of applications.


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