
Computational Statistics

Machine Learning

Statistical Methodology
Computational Statistics
Machine Learning
Statistical Methodology
Faculty
Chris Holmes
Decision theory, biostatistics and precision medicine, probabilistic learning under model misspecification
Geoff Nicholls
Statistical modeling, Bayes Methods, Monte Carlo Methods.
Postdocs
Louis Aslett
Encrypted statistical methods, parallel MCMC methods, high performance computing, reliability theory
Marco Battiston
Bayesian nonparametrics
Luke Kelly
Statistical methods for intractable models
Patrick RubinDelanchy
Data Science, machinelearning, networks, anomaly detection, cybersecurity
ChiehHsi (Jessie) Wu
Computational statistics, machine learning, stratified medicine.
Graduate Students
Frauke Harms
Bayesian nonparametrics, machine learning, stochastic geometry
Leonard Hasenclever
Large scale machine learning, probabilistic inference, deep learning
Ho Chung Leon Law
Kernel methods, machine learning
Thibaut Lienart
Inference on graphical models, expectation propagation, particle methods
Xiaoyu Lu
Machine learning, reinforcement learning, stochastic processes
Simon Lyddon
Bayesian statistics, decision theory, computational statistics, machine learning.
Chris J. Maddison
Probabilistic inference, Monte Carlo methods, neural networks, point processes
Kaspar Märtens
Computational statistics, Bayesian machine learning, multiview learning
Xenia Miscouridou
Bayesian nonparametrics, Machine Learning, Networks
Valerio Perrone
Bayesian nonparametrics, deep learning
Sebastian Schmon
Bayesian Statistics, Computational Statistics, Markov chain Monte Carlo
Stefan Webb
deep learning, deep generative models, probabilistic inference
Matthew Willetts
Large scale machine learning, Bayesian methods, Gaussian processes, Time series segmentation and classification