The statistical theory group works on the investigation of fundamental principles to perform estimation, inference and prediction in large-scale high-dimensional models, with expertise in both asymptotic and non-asymptotic methods, graphical models and causal inference, Monte Carlo methods, learning theory and optimisation for machine learning.

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Faculty

George Deligiannidis

Computational Statistics, Monte Carlo methods

Robin Evans

Graphical models, causality, algebraic statistics

Patrick Rebeschini

Learning theory, Optimization, Implicit Regularization

Judith Rousseau

Bayesian statistics, Asymptotics, Nonparametric statistics

Graduate Students

Tyler Farghly

Learning theory, Optimisation, Monte Carlo methods

Jake Fawkes

Causal Inference, Machine Learning, Fairness