Students

The programme's current students and their areas of research.

Sándor Bartha - 2017 intake Program synthesis, especially type-driven program synthesis. Inductive logic programming, especially meta-interpretive learning. In general, I am also interested in automated reasoning, type systems, SMT solvers, and declarative programming.		Ondrej Bohdal - 2018 intake Theoretical machine learning and its applications (vision, NLP), neural networks, ML safety and explainability.
Asa Cooper Stickland - 2017 intake Approximate Bayesian inference, generative models, Bayesian deep learning, and application of the previous topics to NLP.		Linus Ericsson - 2018 intake Interpretable machine learning, learning-to-learn paradigms and Bayesian deep learning with an interest in applications to energy and healthcare.
Elaine Farrow - 2017 intake Natural language processing and machine learning applied to educational technology. Modelling student engagement in online courses through automated analysis of discussion forum messages.		Sigrid Passano Hellan - 2018 intake Machine learning, optimisation and algorithms, especially as applied to energy problems.
Aidan Marnane - 2018 intake Network Representation Learning, semi-supervised learning on networks, community detection and large scale clustering of attributed graphs. Bio-medical applications of such methods. For example, the analysis of Autism Spectrum Disorder.		Kate McCurdy- 2018 intake Computational linguistics
Julie-Anne Meaney - 2018 intake Computational Linguistics, speech recognition, development of language in children.		Paul Micaelli - 2017 intake Deep learning and neural networks. Particular interest in knowledge distillation, few-shot learning and meta learning.
Kaan Öcal - 2018 intake Stochastic modelling and statistical inference in biology, spatiotemporal models in molecular biophysics, stochastic reaction kinetics.		Katarzyna Prus - 2018 intake Computational linguistics. Natural language processing, particularly for low-resource languages. Semantics of natural language.
James Ritchie - 2017 intake Bayesian approaches to deep learning, approximate inference, probabilistic programming languages and the application of these techniques to real world problems requiring the understanding of uncertainty.		Matt Rounds - 2015 intake Machine learning, deep neural networks, human-like computing, applications to computer vision, applications to neuroinformatics.
Markus Schneider - 2018 intake Databases, especially database theory and query languages; theoretical computer science and logic and their applications to databases; problems that arise in conjunction with big data		Tom Sherborne - 2018 intake Machine learning for natural language processing. Language understanding for interactive models. Domain adaptation and transfer learning for cross-lingual language modelling.
Christine Simpson - 2017 intake Machine learning, Bayesian inference and analysing data from gravitational wave detectors.		William Toner - 2018 intake Mathematical principles of machine learning, the relationship between architectures and domain invariances, networks as function spaces, neural networks and information theory, meta-learning.