The following academic staff are potential supervisors for data science MSc and PhD projects. More details can be found on their individual web pages.

Explainable AI, scalable probabilistic inference and learning, probabilistic programming, statistical relational learning, commonsense reasoning, automated planning, and unifying logic and probability more generally.

Bayesian nonparametric wavelet regression. Bayesian hierarchical modelling of genomics data.

Data visualization and graphical learning; Econometrics; Geometrical statistics; Medical diagnostic assessment; Statistical inferences for small-probability events; Statistics of extremes.

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.

Efficient statistical learning, inference for stochastic dynamical systems, unsupervised deep learning, natural image statistics, computational biology.

Research interests: Bayesian statistics, Nonparametric Statistics, Biostatistics, Functional data analysis, and Statistical Computing.

Bayesian inference; Capture-recapture; Hidden (semi-)Markov models; Missing data; State-space models; Statistical ecology.

Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis.

Probabilistic models, in particular copula-based models; Dimensionality

reduction techniques; Information theory; Applications to biological

systems.

reduction techniques; Information theory; Applications to biological

systems.

Extreme Value Theory and Statistical Inference; Multivariate Analysis and Graphical Models; Spatial statistics; Environmental Science and Biostatistics.

Machine learning, deep learning applied to problems in software testing and program analysis.

Bayesian statistics. Nonparametric statistics, point processes, algorithmic trading, financial markets, natural hazards prediction, cyber security.

Probabilistic modeling of biological systems, dynamics of regulatory networks, computational epigenetics, spatiotemporal systems.

Bayesian and reinforcement learning models of cognition, Computational Psychiatry.

Applied and Computational Mathematics, Probability & Stochastic Analysis and Statistics

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.

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.

Machine learning, probabilistic models, approximate inference, deep learning. Applications to software engineering, programming languages, sustainable energy, & data mining.

Machine learning, image understanding, time series understanding, unsupervised learning, deep learning, Gaussian processes.

Databases: data models, query languages, semistructured data, data provenance, databases & programming languages. Programming languages: functional programming & type systems. Bioinformatics & scientific databases. Mathematical phylogeny.

Databases and data provenance. Programming languages and compilers. Generic programming. Logic and automated theorem proving. Compression and information theory. XML and related technologies.

Database theory and systems: big data, data quality, data integration, database security, distributed query processing, query languages, social networks, Web services and recommendation systems.

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.

Databases with emphasis on query languages, knowledge representation and reasoning, computational logic and its applications to computer science.

Just-in-time SQL compilation, heterogeneous storage, and distributed computing.

Integrating query and programming languages, XML, functional programming, web programming.

Distributed systems, "Big Data" systems, cloud computing, and storage systems.

Algorithms, especially algorithms for counting and sampling. Random structures. Learning theory. Pseudorandom generators.

Algorithms and complexity theory, algorithmic game theory, equilibrium computation, analysis of probabilistic systems, Markov decision processes and stochastic games, analysis of infinite-state systems.

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.

Stochastic Optimization. High Performance Computing. Applications to Energy, Finance and Telecommunications.

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.

Autonomous agents, multi-agent systems, interactive decision making, planning and learning under uncertainty. Applications in areas such as cyber security and self-driving vehicles.

Data visualization, relational data, temporal data, storytelling, immersive visualization.

Computer vision and deep learning: Object classification and detection, human action classification, weakly supervised learning, motion representations, multi-task learning in deep networks.

The automatic construction, analysis and evolution of representations of logical theories also called knowledge bases or ontologies.

Computational linguistics and machine learning, specifically structured prediction. Computational methods for reasoning about natural language and linguistic structure.

Machine-learning techniques for energy-efficient systems. Automated design processes. Software and hardware adaptivity.

Range image and 4D video analysis including 3D model acquisition, video capture and analysis of biological organisms, and iconic image analysis.

Applied machine learning in energy-related areas. End use energy demand. Energy efficiency in buildings.

Machine translation. Algorithms for web-scale data. Scaling statistical models without compromising quality. Approximate dynamic programming for natural language inference problems.

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.

Probabilistic models of cognition, parsing, language production, language acquisition, language vision interface, eyetracking.

Text-to-speech synthesis. The use of articulatory information for speech synthesis and automatic speech recognition. Speech perception and production.

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.

Probabilistic learning. Natural language understanding and generation. Information extraction.

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.

Statistical models for machine translation and language understanding, formal language theory, structured prediction, and algorithms.

Causal inference, generalization, & latent variable discovery by human learners and machines . Hierarchical and nonparametric Bayesian models for abstract learning and knowledge transfer.

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.

Information Retrieval: social search, patent search, evaluation metrics, Arabic IR, and CLIR.

Data Mining: text mining, classification, and sentiment analysis.

Computational linguistics: natural language generation, dialogue, and discourse. Intelligent systems for education. Personalised information presentation, multi-modal interaction, user modeling, and knowledge representation.

Automatic discourse generation diagrammatic reasoning and communication individual differences in interaction.

Design and deployment of multi-agent systems; large-scale, automated design and transformation of knowledge bases and problem solvers; agent-oriented software engineering.

Intelligent agents and multiagent systems: reasoning about interaction, multiagent planning, collaborative learning, combination of knowledge-based and game-theoretic techniques, social computing.

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.

Trainable lifelike conversational agents, Acoustic models for automatic speech recognition, Handwriting recognition.

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).

Computational linguistics, artificial intelligence, formal grammar, semantics, spoken intonation, statistical parsing, spoken language processing, animated conversational agents, and computational musical analysis.

Computational models of discourse, statistical machine translation (SMT), semantics for SMT.

Speech information processing. Statistical speech synthesis. Machine learning, speech production, and linguistics.

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.

Computer security; programming languages and their semantics and logics; probabilistic programming for machine learning.

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.