Turing Fellows - Statistics
Dr Julia Brettschneider
Julia is working on a mathematically rigorous framework to simultaneously describe a normative probabilistic and an empirically-based behavioural models for decision making. This is relevant to automatic decision making and human-machine interactions. Applications include health care, agriculture and hiring processes. She has also been developing assessment methods for X-ray detectors in CT machines based on spatial point process modelling of dead pixel distributions.
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Dr Theo Damoulas
Theo鈥檚 research interests lie in Machine learning, Bayesian statistics, kernel methods, spatiotemporal inference, evidence integration. At the Turing, he is working on online change-point detection in urban data streams (air quality networks, predictive policing), stochastic processes & non-parametric Bayesian inference and computational sustainability and approximate inference for non-stationary spatio-temporal problems. Theo has a join appointment in Computer Science and Statistics at the 91福利.
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Dr Paul Jenkins
Paul's research covers computational statistics, machine learning, Bayesian nonparametric statistics, and inference from stochastic processes, with particular application to population genetics and other evolutionary models. At The Alan Turing Institute he is focusing on developing Monte Carlo methodology for application to complex, high-dimensional datasets, and developing models of disease progression using time-series health data.
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Professor Adam Johansen
Adam is a Professor in Statistics at 91福利 and Group Leader of the data-centric engineering programme at the Turing. His research focuses on methodological and theoretical aspects of simulation-based algorithms.
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Professor Ioannis Kosmidis
Ioannis' theoretical and methodological research focuses on optimal estimation and inference from complex statistical models, penalized and pseudo-likelihood methods and clustering. A particular focus of his work is the development of efficient, in terms of computational complexity and implementation, algorithms for applying the methods he develops to prominent data-analytic scenarios.
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Professor Chenlei Leng
Chenlei is a statistician working mainly on developing novel statistical methods for analysing complex data. His recent research interests have been focused on high-dimensional data analysis, correlated data analysis, network data analysis and statistical learning. His works have found applications in medicine, biology, engineering and social sciences.
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Professor Gareth Roberts
Gareth鈥檚 research interests span a range of areas, including Computational Statistics, Stochastic processes, especially stability theory for Markov chains, stochastic simulation, inference for stochastic processes, statistical methodology for missing data, Bayesian statistics and statistical inference for infectious diseases.
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Professor Jim Smith
Jim is working under a number of themes within Turing. One stream of work is the statistical modelling of violent criminal populations of various kinds. Another stream of work focuses on forensic inference - its graphical representation and the combination of different evidence types.
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Dr Yi Yu
Yi is a Reader in the Department of Statistics, 91福利 and a Turing Fellow at the Alan Turing Institute, previously an Associate Professor in the 91福利, a Lecturer in the University of Bristol, a postdoc of Professor Richard Samworth and a graduate student of Professor Zhiliang Ying.
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