The following academic staff are potential supervisors for data science MSc and PhD projects. More details can be found on their individual web pages.
Machine Learning & Statistics
Vaishak Belle
Explainable AI, scalable probabilistic inference and learning, probabilistic programming, statistical relational learning, commonsense reasoning, automated planning, and unifying logic and probability more generally.
Natalia Bochkina
Bayesian nonparametric wavelet regression. Bayesian hierarchical modelling of genomics data.
Tim Cannings
Research interests: Statistical learning for Big Data; high-dimensional classification problems; nonparametric methods; data perturbation techniques; noisy and incomplete data; applications in genomics and cancer therapy. Miguel de Carvalho
Data visualization and graphical learning; Econometrics; Geometrical statistics; Medical diagnostic assessment; Statistical inferences for small-probability events; Statistics of extremes.
Chris Dent
Applications of data science in the energy industry and in government. Electricity security of supply risk assessment. Modelling of renewable resource. Methodology for use of modelling in capital planning and policy decision support. Model calibration and uncertainty quantification. Communication of risk and uncertainty.
Goncalo dos Reis
Probabilistic modeling of financial risk to capture non-linear non-markovian effect; numerical probability; numerical stochastics and statisticsJonathan Gair
Big data in the physical sciences; analysis & exploitation of data from gravitational wave detectors; quantifying uncertainty in inference based on complex models; statistical emulation in the physical, biological & environmental sciences.
Michael Gutmann
Efficient statistical learning, inference for stochastic dynamical systems, unsupervised deep learning, natural image statistics, computational biology.Vanda Inacio
Research interests: Bayesian statistics, Nonparametric Statistics, Biostatistics, Functional data analysis, and Statistical Computing.
Ruth King
Bayesian inference; Capture-recapture; Hidden (semi-)Markov models; Missing data; State-space models; Statistical ecology.
Benedict Leimkuhler
Stochastic algorithms for high dimensional sampling and statistical calculation; Bayesian parameterisation of neural networks.Finn Lindgren
Spatio-temporal modelling; Large scale computational statistics and statistical software; Gaussian processes; Markov random fields; Numerical methods for stochastic partial differential equations; Approximate Bayesian inference; Climate reconstruction; Statistical ecology.Iain Murray
Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis.
Arno Onken
Probabilistic models, in particular copula-based models; Dimensionalityreduction techniques; Information theory; Applications to biological
systems.
Ioannis Papastathopoulos
Extreme Value Theory and Statistical Inference; Multivariate Analysis and Graphical Models; Spatial statistics; Environmental Science and Biostatistics.
Ajitha Rajan
Machine learning, deep learning applied to problems in software testing and program analysis.
Gordon Ross
Bayesian statistics. Nonparametric statistics, point processes, algorithmic trading, financial markets, natural hazards prediction, cyber security.
Sotirios Sabanis
Analysis of Langevin Monte Carlo sampling & optimization algorithms (including the unbiased estimators' version, e.g. SGLD) in a non-Markovian environment, i.e. observation data need not to be Markovian, and their applications in Bayesian learning/inference. Any application of these methodologies to financial data is of keen interest to me.Guido Sanguinetti
Probabilistic modeling of biological systems, dynamics of regulatory networks, computational epigenetics, spatiotemporal systems.
Peggy Series
Bayesian and reinforcement learning models of cognition, Computational Psychiatry.
Lukasz Spruch
Applied and Computational Mathematics, Probability & Stochastic Analysis and Statistics
Amos Storkey - Director
Structured machine learning & big data: Bayesian methods, Machine Learning Markets, learning representations & structure, deep learning, models for sequences & connections to neural computation. Applications in images, brain imaging, medicine & signal processing.
Kartic Subr
Analysis of Monte Carlo sampling, stochastic signal processing and numerical integration of high-dimensional discontinuous functions with applications in light transport simulation for computer graphics, image processing and computational photography.
Aretha Teckentrup
Applied mathematics, Bayesian inverse problems, Uncertainty Quantification, Gaussian processes, Numerical Analysis, Sampling methods in high dimensions.
Sara Wade
Bayesian nonparametrics, machine learning, clustering, feature allocation, density regression, dimension reduction, Gaussian processes, MCMC, variational inference, and MAP inference.Chris Williams
Machine learning, image understanding, time series understanding, unsupervised learning, deep learning, Gaussian processes.
Amy Wilson
Quantifying uncertainty in complex computer models, statistical emulation, statistical modelling for energy policy, statistics and the law, forensic evidence evaluation.
Kostas Zygalakis
Computational statistics, stochastic optimisation, stochastic differential equations, probabilistic numerics.
Databases
Peter Buneman
Databases: data models, query languages, semistructured data, data provenance, databases & programming languages. Programming languages: functional programming & type systems. Bioinformatics & scientific databases. Mathematical phylogeny.
James Cheney
Databases and data provenance. Programming languages and compilers. Generic programming. Logic and automated theorem proving. Compression and information theory. XML and related technologies.
Wenfei Fan
Database theory and systems: big data, data quality, data integration, database security, distributed query processing, query languages, social networks, Web services and recommendation systems.
Leonid Libkin
Databases: query languages, relational, XML, and graph data, constraints and design, data integration and exchange, incomplete information. Logic in computer science, finite model theory, automata theory.
Milos Nikolic - Co Director
Databases and large-scale data management systems: in-database learning, stream processing, incremental computation, query compilation
Andreas Pieris
Databases with emphasis on query languages, knowledge representation and reasoning, computational logic and its applications to computer science.
Philip Wadler
Integrating query and programming languages, XML, functional programming, web programming.
Algorithms & Big Data
Pramod Bhatotia
Distributed systems, "Big Data" systems, cloud computing, and storage systems.Mary Cryan
Algorithms, especially algorithms for counting and sampling. Random structures. Learning theory. Pseudorandom generators.
Kousha Etessami
Algorithms and complexity theory, algorithmic game theory, equilibrium computation, analysis of probabilistic systems, Markov decision processes and stochastic games, analysis of infinite-state systems.
Sergio Garcia Quiles
Integer and combinatorial optimization. Exact algorithms. Heuristic algorithms. Applications to logistics, matching, and clustering.Jacek Gondzio
Optimization methods for linear, quadratic and nonlinear programming. Linear algebra techniques and sparse matrix factorisation methods for optimization. Very large optimization problems in telecommunications, energy and finance.
Andreas Grothey
Stochastic Optimization. High Performance Computing. Applications to Energy, Finance and Telecommunications.
Julian Hall
Optimization methods for linear and quadratic programming. Numerical linear algebra techniques for sparse matrices in large scale computational optimization. Industrial applications in feed formulation, genomics, telecommunications, petrochemicals, data science and finance.Joerg Kalcsics
Exact and heuristic models and algorithms for solving large-scale integer and combinatorial optimization problems, with a focus on supply chain management and service scheduling. Combining optimization, complexity theory, algorithmic design, and computational geometry to obtain structural results and efficient solution approaches.
John Pearson
Linear algebra and optimization algorithms for huge-scale systems; PDE-constrained optimization problems, including stochastic control problems; numerical linear algebra for data science; applications of data science in scientific and industrial processes
Peter Richtarik
Design and analysis of optimization algorithms for big data. Randomized, parallel and distributed methods. Gradient descent, stochastic gradient descent and coordinate descent. Supercomputing. Optimization in machine learning. Applications in industry, engineering, astronomy, biology and finance.
Unstructured Data & Applications
Stefano Albrecht
Autonomous agents, multi-agent systems, interactive decision making, planning and learning under uncertainty. Applications in areas such as cyber security and self-driving vehicles.Benjamin Bach
Data visualization, relational data, temporal data, storytelling, immersive visualization.
Hakan Bilen
Computer vision and deep learning: Object classification and detection, human action classification, weakly supervised learning, motion representations, multi-task learning in deep networks.
Alan Bundy
The automatic construction, analysis and fault repair of representations of logical theories, also called knowledge bases or ontologies. Automatic discovery of novel knowledge by combining existing knowledge from the Web, using both deductive and statistical reasoning.
Shay Cohen
Computational linguistics and machine learning, specifically structured prediction. Computational methods for reasoning about natural language and linguistic structure.
Christophe Dubach
Machine-learning techniques for energy-efficient systems. Automated design processes. Software and hardware adaptivity.
Bob Fisher
Range image and 4D video analysis including 3D model acquisition, video capture and analysis of biological organisms, and iconic image analysis.
Kobi Gal
Artificial Intelligence, Machine Learning for Human-Computer Collaboration and Negotiation, Big data in Education, Plan and Goal Recognition, Collaborative Group Learning, Incentive Design for effective teamwork, Computational Cognitive Science, Intelligent and Adaptive Tutoring Systems, Computational Game Theory
Dorota Glowacka
Personalisation, exploratory search, experimental design and evaluation methodology.
Nigel Goddard
Applied machine learning in energy-related areas. End use energy demand. Energy efficiency in buildings.
Learning linguistic structure in humans and machines.
Kenneth Heafield
Machine translation. Algorithms for web-scale data. Scaling statistical models without compromising quality. Approximate dynamic programming for natural language inference problems.
Timothy Hospedales
Machine Learning: Lifelong, transfer and multi-task learning, active and curriculum learning. Computer Vision: Deep learning, vision and language, object, action and person recognition. Robot Learning: Business and financial analytics.
Frank Keller
Probabilistic models of cognition, parsing, language production, language acquisition, language vision interface, eyetracking.
Simon King
Text-to-speech synthesis. The use of articulatory information for speech synthesis and automatic speech recognition. Speech perception and production.
Ewan Klein
Computational approaches to natural language semantics, syntax, prosody and phonology. Spoken language processing. Communicating with mobile robots and embodied devices. The Semantic Web and ontologies. Open data.
Mirella Lapata
Probabilistic learning. Natural language understanding and generation. Information extraction.
Alex Lascarides
Computational semantics and pragmatics, discourse and dialogue processing, communication with non-verbal actions, machine learning models of action and decision making, learning strategies in highly complex games.
Adam Lopez
Statistical models for machine translation and language understanding, formal language theory, structured prediction, and algorithms.
Christopher Lucas
Causal inference, generalization, & latent variable discovery by human learners and machines . Hierarchical and nonparametric Bayesian models for abstract learning and knowledge transfer.
Walid Magdy
Social Computing: social content analysis, political bias detection, and users' behavior prediction.Information Retrieval: social search, patent search, evaluation metrics, Arabic IR, and CLIR.
Data Mining: text mining, classification, and sentiment analysis.
Johanna Moore
Computational linguistics: natural language generation, dialogue, and discourse. Intelligent systems for education. Personalised information presentation, multi-modal interaction, user modeling, and knowledge representation.
Paul Patras
Deep learning for mobile data traffic and urban analytics. Unsupervised learning applications to network security.
Speech technology, multimodal interaction, spoken language processing.
Dave Robertson
Design and deployment of multi-agent systems; large-scale, automated design and transformation of knowledge bases and problem solvers; agent-oriented software engineering.
Michael Rovatsos
Intelligent agents and multiagent systems: reasoning about interaction, multiagent planning, collaborative learning, combination of knowledge-based and game-theoretic techniques, social computing.
Rik Sarkar
Distributed algorithms for processing spatial and location data, computational geometry and topology. Sensing and inference in mobile devices, sensor networks. Analysis of large networks. Distributed optimization.
Machine translation, natural language processing, deep learning.
Hiroshi Shimodaira
Trainable lifelike conversational agents, Acoustic models for automatic speech recognition, Handwriting recognition.
Ian Simpson
Neuroregulatory genomics, computational biology, statistics &machine-learning. Molecular control of neural development &function in relation to cognition, learning &memory. Evolution &conservation of molecular regulatory processes. Analysis of high-throughput data-sets (genomic, meta-genomic, transcriptomic &proteomic).
Mark Steedman
Computational linguistics, artificial intelligence, formal grammar, semantics, spoken intonation, statistical parsing, spoken language processing, animated conversational agents, and computational musical analysis.
Bonnie Webber
Computational models of discourse, statistical machine translation (SMT), semantics for SMT.
Junichi Yamagishi
Speech information processing. Statistical speech synthesis. Machine learning, speech production, and linguistics.
Risk, Security, Privacy & Ethical Issues
David Aspinall
Applications of machine learning to cyber security, such as software and malware analysis, user interaction in security, authentication, trust and reputation. Software verification and theorem proving.
Andrew Gordon
Computer security; programming languages and their semantics and logics; probabilistic programming for machine learning.
Tiejun Ma
Risk analysis and decision-making using quantitative modelling and real-time Big data analysis techniques applies to FinTech, Cyber-Risk and Resilience of distributed systems. He employs applied data science and mathematical modelling methodologies in the analysis/forecasts of risks, using an inter-disciplinary research strategy, via state-of-the-art computing, data analytics and behavioural analysis techniques.
Kami Vaniea
Usable security and privacy. Human Computer Interaction. Supporting decision making through feedback. Making security and privacy technologies easier to use by understanding how people interact with them.