Management Team
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The following faculty form the Supervisory Pool for OxWaSP:
Dr Cedric Archambeau (Amazon, Berlin, Germany)
Probabilistic machine learning, approximate inference, Bayesian optimization; representation learning, transfer learning, continual learning, and resource-efficient learning.
[email protected]
Probabilistic machine learning, approximate inference, Bayesian optimization; representation learning, transfer learning, continual learning, and resource-efficient learning.
[email protected]
Professor Julien Berestycki (Oxford)
Branching processes, branching random walks, coalescence, fragmentation, population genetics, reaction-diffusion equations, front propagation, random trees.
[email protected]
Branching processes, branching random walks, coalescence, fragmentation, population genetics, reaction-diffusion equations, front propagation, random trees.
[email protected]
Dr Julia Brettschneider (Warwick)
Statistical methodology for high-dimensional molecular data. Methodology for analysis of high-throughput genomic and proteomic data.
J.A.Brettschneider@warwick.ac.uk
Statistical methodology for high-dimensional molecular data. Methodology for analysis of high-throughput genomic and proteomic data.
J.A.Brettschneider@warwick.ac.uk
Professor Francois Caron (Oxford)
Bayesian nonparametrics, computational statistics, statistical machine learning, networks.
[email protected]
Bayesian nonparametrics, computational statistics, statistical machine learning, networks.
[email protected]
Dr Mingli Chen (Warwick)
My research fields are Econometrics, Time Series Econometrics, Financial Econometrics and Industrial Organization.
[email protected]
My research fields are Econometrics, Time Series Econometrics, Financial Econometrics and Industrial Organization.
[email protected]
Professor Mihai Cucuringu (Oxford)
Development and mathematical & statistical analysis of algorithms that extract information from massive noisy data. Inverse problems on large graphs with applications in machine learning and network analysis. Spectral and semidefinite programming algorithms for ranking, clustering, group synchronization, phase unwrapping. Time series analysis. Statistical analysis of financial data, statistical arbitrage, limit order books, risk models. [email protected]
Development and mathematical & statistical analysis of algorithms that extract information from massive noisy data. Inverse problems on large graphs with applications in machine learning and network analysis. Spectral and semidefinite programming algorithms for ranking, clustering, group synchronization, phase unwrapping. Time series analysis. Statistical analysis of financial data, statistical arbitrage, limit order books, risk models. [email protected]
Dr Theo Damoulas (Warwick)
My research interests are in machine learning and Bayesian statistics with a focus on spatio-temporal problems in urban science and computational sustainability.
[email protected]
My research interests are in machine learning and Bayesian statistics with a focus on spatio-temporal problems in urban science and computational sustainability.
[email protected]
Professor Charlotte Deane (Oxford)
Protein structure evolution. Protein structure prediction. Analysis of protein-protein interactions.
[email protected]
Protein structure evolution. Protein structure prediction. Analysis of protein-protein interactions.
[email protected]
Professor George Deligiannidis (Oxford)
Computational statistics, analysis of Monte Carlo methods, limit theorems for stochastic processes, random walks in lattices.
[email protected]
Computational statistics, analysis of Monte Carlo methods, limit theorems for stochastic processes, random walks in lattices.
[email protected]
Professor Xavier Didelot (Warwick)
Statistical genetics, infectious disease epidemiology, Bayesian inference, Monte Carlo methods.
[email protected]
Statistical genetics, infectious disease epidemiology, Bayesian inference, Monte Carlo methods.
[email protected]
Professor Arnaud Doucet (Oxford)
Bayesian statistics; Stochastic Simulation; Sequential Monte Carlo; Markov Chain Monte Carlo; Time Series.
[email protected]
Bayesian statistics; Stochastic Simulation; Sequential Monte Carlo; Markov Chain Monte Carlo; Time Series.
[email protected]
Dr Rito Dutta (Warwick)
Likelihood-free Inference, Approximate Bayesian Computations, Mechanistic Network Models, Model Selection, Applications of Statistics in Natural Science (e.g. Epidemiology, Bio-simulation etc.).
[email protected]
Likelihood-free Inference, Approximate Bayesian Computations, Mechanistic Network Models, Model Selection, Applications of Statistics in Natural Science (e.g. Epidemiology, Bio-simulation etc.).
[email protected]
Professor Alison Etheridge (Oxford)
Infinite dimensional stochastic processes and their applications. Mathematical problems in population genetics.
[email protected]
Infinite dimensional stochastic processes and their applications. Mathematical problems in population genetics.
[email protected]
Professor Robin Evans (Oxford)
My research is geared towards detecting and quantifying causal mechanisms from non-experimental data, and understanding when this is impossible. To that end, I work mainly on graphical models, algebraic statistics and model selection problems.
[email protected]
My research is geared towards detecting and quantifying causal mechanisms from non-experimental data, and understanding when this is impossible. To that end, I work mainly on graphical models, algebraic statistics and model selection problems.
[email protected]
Professor Bärbel Finkenstädt Rand (Warwick)
Time series analysis and dynamical systems. Periodic time series and oscillations in biological systems. Parameter estimation for (stochastic) differential equations.
[email protected]
Time series analysis and dynamical systems. Periodic time series and oscillations in biological systems. Parameter estimation for (stochastic) differential equations.
[email protected]
Professor David Firth (Warwick)
Statistical theory and methods, including design and computation. Generalized linear and non-linear models. Applications, especially in the social and health sciences.
[email protected]
Statistical theory and methods, including design and computation. Generalized linear and non-linear models. Applications, especially in the social and health sciences.
[email protected]
Professor Yarin Gal (Oxford)
Machine learning, Bayesian deep learning, approximate Bayesian inference, Gaussian processes, Bayesian non-parametrics, generative modelling, AI safety, ML interpretability, reinforcement learning, active learning, natural language processing, computer vision, medical analysis.
[email protected]
Machine learning, Bayesian deep learning, approximate Bayesian inference, Gaussian processes, Bayesian non-parametrics, generative modelling, AI safety, ML interpretability, reinforcement learning, active learning, natural language processing, computer vision, medical analysis.
[email protected]
Professor Jotun Hein (Oxford)
Sequence Analysis, Genome Annotation and Statistical Alignment. Phylogenies, Pedigrees and Recombination. Integrative Genomics and Data Fusion. Comparative Biology
[email protected]
Sequence Analysis, Genome Annotation and Statistical Alignment. Phylogenies, Pedigrees and Recombination. Integrative Genomics and Data Fusion. Comparative Biology
[email protected]
Dr Vicky Henderson (Warwick)
Optimal stopping and optimal control problems, with applications to real options, executive stock options, and recently, behavioural finance
[email protected]
Optimal stopping and optimal control problems, with applications to real options, executive stock options, and recently, behavioural finance
[email protected]
Dr Martin Herdegen (Warwick)
Arbitrage theory, change of numéraire, utility maximisation, financial bubbles, transaction costs, equilibria (with and without frictions). Semimartingale calculus, strict local martingales, processes with jumps.
[email protected]
Arbitrage theory, change of numéraire, utility maximisation, financial bubbles, transaction costs, equilibria (with and without frictions). Semimartingale calculus, strict local martingales, processes with jumps.
[email protected]
Professor Chris Holmes (Oxford)
Bayesian Statistics. Statistical genetics and genomics. Methods and computation for big data. Nonparametric regression and machine learning methods.
[email protected]
Bayesian Statistics. Statistical genetics and genomics. Methods and computation for big data. Nonparametric regression and machine learning methods.
[email protected]
Professor Jane Hutton (Warwick)
Missing data, chain event graphs, life time data analysis.
[email protected]
Missing data, chain event graphs, life time data analysis.
[email protected]
Professor Saul Jacka (Warwick)
Stochastic differential equations. Stochastic control. Applied stochastic processes. Optimal stopping. Applications of probability in finance and economics.
[email protected]
Stochastic differential equations. Stochastic control. Applied stochastic processes. Optimal stopping. Applications of probability in finance and economics.
[email protected]
Dr Paul Jenkins (Warwick)
Monte Carlo methods, mathematical genetics, Bayesian nonparametrics.
[email protected]
Monte Carlo methods, mathematical genetics, Bayesian nonparametrics.
[email protected]
Dr Adam Johansen (Warwick)
Monte Carlo Methods (particularly Sequential Monte Carlo), Computational statistics, Bayesian inference (particularly for time series).
[email protected]
Monte Carlo Methods (particularly Sequential Monte Carlo), Computational statistics, Bayesian inference (particularly for time series).
[email protected]
Prof Varun Kanade (Oxford)
Computational and statistical learning theory; machine learning theory; online learning; randomized algorithms; theoretical computer science.
[email protected]
Computational and statistical learning theory; machine learning theory; online learning; randomized algorithms; theoretical computer science.
[email protected]
Professor Wilfrid Kendall (Warwick)
Stochastic differential equations. Computer algebra in probability and statistics. Applied probability especially in relation to spatial statistics.
[email protected]
Stochastic differential equations. Computer algebra in probability and statistics. Applied probability especially in relation to spatial statistics.
[email protected]
Dr Jo Kennedy (Warwick)
Financial mathematics. Probability theory. Duality and time-change problems.
[email protected]
Financial mathematics. Probability theory. Duality and time-change problems.
[email protected]
Dr Jere Koskela (Warwick)
Monte Carlo methods, inference from diffusions and other stochastic processes, coalescent processes, mathematical population genetics
[email protected]
Monte Carlo methods, inference from diffusions and other stochastic processes, coalescent processes, mathematical population genetics
[email protected]
Professor Pawan Kumar (Oxford)
Continuous and discrete optimisation for machine learning; machine learning for continuous and discrete optimisation.
[email protected]
Continuous and discrete optimisation for machine learning; machine learning for continuous and discrete optimisation.
[email protected]
Dr Krzystof Łatuszyński (Warwick)
Markov chain Monte Carlo, adaptive Monte Carlo, stochastic simulations and Bayesian statistics.
[email protected]
Markov chain Monte Carlo, adaptive Monte Carlo, stochastic simulations and Bayesian statistics.
[email protected]
Professor Chenlei Leng (Warwick)
Large-scale interference, interface between computational scalability and statistical optimality, statistics of networks.
[email protected]
Large-scale interference, interface between computational scalability and statistical optimality, statistics of networks.
[email protected]
Professor James Martin (Oxford)
Probability, interacting particle systems, models of random growth and percolation, processes of coagulation and fragmentation, random graphs.
[email protected]
Probability, interacting particle systems, models of random growth and percolation, processes of coagulation and fragmentation, random graphs.
[email protected]
Dr Patrick McSharry (Oxford)
Time series modelling, machine learning, signal processing, and systems analysis.
[email protected]
Time series modelling, machine learning, signal processing, and systems analysis.
[email protected]
Professor Alex Mijatovic (Warwick)
Numerical stochastics, mathematical finance, statistics, probability.
[email protected]
Numerical stochastics, mathematical finance, statistics, probability.
[email protected]
Professor Simon Myers (Oxford)
Population genetics. Methods to map disease genes in admixed populations, and methods for fine-mapping association signals.
[email protected]
Population genetics. Methods to map disease genes in admixed populations, and methods for fine-mapping association signals.
[email protected]
Professor Geoff Nicholls (Oxford)
Bayesian inference, Statistical Methods, Computational Statistics, Monte Carlo, Statistical Genetics, Applied Statistics.
[email protected]
Bayesian inference, Statistical Methods, Computational Statistics, Monte Carlo, Statistical Genetics, Applied Statistics.
[email protected]
Professor Thomas Nichols (Oxford)
Statistical methods for the analysis of brain image data.
[email protected]
Statistical methods for the analysis of brain image data.
[email protected]
Dr Mike Osborne (Oxford)
Probabilistic numerics. Bayesian inference. Gaussian processes.
[email protected]
Probabilistic numerics. Bayesian inference. Gaussian processes.
[email protected]
Professor Pier Francesco Palamara (Oxford)
Computational methods for population genetics (natural selection, demographic history); statistical genetics (complex trait heritability, association); scalable methods for large genomic data sets.
[email protected]
Computational methods for population genetics (natural selection, demographic history); statistical genetics (complex trait heritability, association); scalable methods for large genomic data sets.
[email protected]
Dr Anastasia Papavasiliou (Warwick)
Applied probability. Stochastic filtering and control. Theory of rough paths. Applications to signal processing. Multiscale systems.
[email protected]
Applied probability. Stochastic filtering and control. Theory of rough paths. Applications to signal processing. Multiscale systems.
[email protected]
Professor Martyn Plummer (Warwick)
Biostatistics, cancer epidemiology, statistical computing (particularly R), and Markov Chain Monte Carlo methods.
[email protected]
Biostatistics, cancer epidemiology, statistical computing (particularly R), and Markov Chain Monte Carlo methods.
[email protected]
Dr Murray Pollock (Warwick)
Algorithmic design; Bayesian inference; Monte Carlo methodology; Perfect simulation; Scalable methods for big data and big models; Stochastic differential equations.
[email protected]
Algorithmic design; Bayesian inference; Monte Carlo methodology; Perfect simulation; Scalable methods for big data and big models; Stochastic differential equations.
[email protected]
Professor Patrick Rebeschini (Oxford)
Algorithms: message-passing; distributed, online, and stochastic optimization; bandits; boosting; Monte Carlo in high-dimension.
[email protected]
Algorithms: message-passing; distributed, online, and stochastic optimization; bandits; boosting; Monte Carlo in high-dimension.
[email protected]
Professor Gesine Reinert (Oxford)
Probabilistic and statistical methods for network analysis. Stein's method and the quantification of approximations.
[email protected]
Probabilistic and statistical methods for network analysis. Stein's method and the quantification of approximations.
[email protected]
Professor Christian Robert (Warwick)
Approximate Bayesian methods, aimed at complex and intractable likelihoods, towards producing manageable and convergent inferential solutions. Foundations of Bayesian testing and model choice, towards producing a more all-inclusive resolution compared with the Bayes solutions inspired from the Neyman-Pearson universe. Acceleration of MCMC methods, during and after simulation, by means of reparameterisation, coupling, Rao-Blackwellisation, and delayed acceptance.
[email protected]
Approximate Bayesian methods, aimed at complex and intractable likelihoods, towards producing manageable and convergent inferential solutions. Foundations of Bayesian testing and model choice, towards producing a more all-inclusive resolution compared with the Bayes solutions inspired from the Neyman-Pearson universe. Acceleration of MCMC methods, during and after simulation, by means of reparameterisation, coupling, Rao-Blackwellisation, and delayed acceptance.
[email protected]
Professor Gareth Roberts (Warwick)
I work on theory, methodology and applications of computational statistics, especially Markov chain Monte Carlo. Specific current interests include continuous time and non-reversible MCMC, adaptive methods in computational statistics, scaling limits and complexity of high-dimensional algorithms, and Bayesian inference for stochastic processes.
[email protected]
I work on theory, methodology and applications of computational statistics, especially Markov chain Monte Carlo. Specific current interests include continuous time and non-reversible MCMC, adaptive methods in computational statistics, scaling limits and complexity of high-dimensional algorithms, and Bayesian inference for stochastic processes.
[email protected]
Professor Stephen Roberts (Oxford)
Application of machine learning to huge astrophysical data sets, biodiversity monitoring and finance.
[email protected]
Application of machine learning to huge astrophysical data sets, biodiversity monitoring and finance.
[email protected]
Professor Judith Rousseau (Oxford)
Bayesian statistics ; Bayesian nonparametrics and high dimensional statistics ; Asymptotic properties of Bayesian procedures; measures of uncertainty; model selection; mixture and Hidden Markov models; Point processes; networks; statistical machine learning; computational statistics.
j[email protected]
Bayesian statistics ; Bayesian nonparametrics and high dimensional statistics ; Asymptotic properties of Bayesian procedures; measures of uncertainty; model selection; mixture and Hidden Markov models; Point processes; networks; statistical machine learning; computational statistics.
j[email protected]
Dr Richard Savage (Warwick)
My main research interest is statistical machine learning and its application to important problems in medicine, in particular early cancer detection. This principally involves the use of probabilistic methods for extracting scientific knowledge from large, potentially complex data-sets.
[email protected]
My main research interest is statistical machine learning and its application to important problems in medicine, in particular early cancer detection. This principally involves the use of probabilistic methods for extracting scientific knowledge from large, potentially complex data-sets.
[email protected]
Dr Dino Sejdinovic (Oxford)
Statistical machine learning. Kernel methods. Hypothesis testing with big data. Measures of association and multivariate interaction. Trade-offs between computational and statistical efficiency.
[email protected]
Statistical machine learning. Kernel methods. Hypothesis testing with big data. Measures of association and multivariate interaction. Trade-offs between computational and statistical efficiency.
[email protected]
Professor Jim Smith (Warwick)
High-dimensional environmental modelling. Multi-agent Bayesian decision theory in high dimensional models. High-dimensional business time series. Dynamic Bayes nets.
[email protected]
High-dimensional environmental modelling. Multi-agent Bayesian decision theory in high dimensional models. High-dimensional business time series. Dynamic Bayes nets.
[email protected]
Dr Dario Spanò (Warwick)
Mathematical population genetics. Bayesian non-parametric statistics. Combinatorial stochastic processes. Measure-valued processes.
[email protected]
Mathematical population genetics. Bayesian non-parametric statistics. Combinatorial stochastic processes. Measure-valued processes.
[email protected]
Dr Chris Spencer (Oxford)
Genetics and genomics. Bioinformatics, SNP typing and Statistical genetics.
[email protected]
Genetics and genomics. Bioinformatics, SNP typing and Statistical genetics.
[email protected]
Dr Simon Spencer (Warwick)
Bayesian inference, stochastic processes and applied probability, MCMC methods
[email protected]
Bayesian inference, stochastic processes and applied probability, MCMC methods
[email protected]
Professor Mark Steel (Warwick)
Bayesian statistics and econometrics. Modelling of skewness. Spatial statistics. Model uncertainty. Semi- and nonparametric Bayes.
[email protected]
Bayesian statistics and econometrics. Modelling of skewness. Spatial statistics. Model uncertainty. Semi- and nonparametric Bayes.
[email protected]
Dr Shahin Tavakoli (Warwick)
I am interested in both theory, methods, and applications of statistics. My area of expertise is Functional Data Analysis, but I am also interested in High-Dimensional Statistics, Stochastic Processes, Time Series Analysis, Multiple Testing, and Shrinkage Estimation. Recently, I've become increasingly interested in methodological developments driven by applications in Phonetics, Imaging, and Biophysics.
[email protected]
I am interested in both theory, methods, and applications of statistics. My area of expertise is Functional Data Analysis, but I am also interested in High-Dimensional Statistics, Stochastic Processes, Time Series Analysis, Multiple Testing, and Shrinkage Estimation. Recently, I've become increasingly interested in methodological developments driven by applications in Phonetics, Imaging, and Biophysics.
[email protected]
Professor Yee Whye Teh (Oxford)
Bayesian nonparametrics, probabilistic learning, deep learning.
[email protected]
Bayesian nonparametrics, probabilistic learning, deep learning.
[email protected]
Dr Sebastian Vollmer (Warwick)
Monte Carlo Methods, Stochastic Gradient Methods, Stochastic Processes
[email protected]
Monte Carlo Methods, Stochastic Gradient Methods, Stochastic Processes
[email protected]
Dr Jon Warren (Warwick)
Brownian motion. Local times. Branching processes. Dynamical systems.
[email protected]
Brownian motion. Local times. Branching processes. Dynamical systems.
[email protected]
