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.

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.

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

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.

Probabilistic modeling of financial risk to capture non-linear non-markovian effect; numerical probability; numerical stochastics and statistics

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.

Stochastic algorithms for high dimensional sampling and statistical calculation; Bayesian parameterisation of neural networks.

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.

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.

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.

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.

Applied mathematics, Bayesian inverse problems, Uncertainty Quantification, Gaussian processes, Numerical Analysis, Sampling methods in high dimensions.

Bayesian nonparametrics, machine learning, clustering, feature allocation, density regression, dimension reduction, Gaussian processes, MCMC, variational inference, and MAP inference.

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

Quantifying uncertainty in complex computer models, statistical emulation, statistical modelling for energy policy, statistics and the law, forensic evidence evaluation.

Computational statistics, stochastic optimisation, stochastic differential equations, probabilistic numerics.

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 and large-scale data management systems: in-database learning, stream processing, incremental computation, query compilation

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

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.

Integer and combinatorial optimization. Exact algorithms. Heuristic algorithms. Applications to logistics, matching, and clustering.

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.

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.

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.

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

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 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.

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.

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

Personalisation, exploratory search, experimental design and evaluation methodology.

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.

Deep learning for mobile data traffic and urban analytics. Unsupervised learning applications to network security.

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.

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.

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.