
Computational Statistics

Machine Learning

Statistical Methodology
Computational Statistics
Machine Learning
Statistical Methodology
Faculty
François Caron
Statistical Machine Learning, Bayesian methods, Bayesian nonparametrics, Statistical Network Analysis
Arnaud Doucet
Computational Statistics, Monte Carlo methods
Chris Holmes
Decision theory, biostatistics and precision medicine, probabilistic learning under model misspecification
Patrick Rebeschini
Distributed machine learning
Dino Sejdinovic
Statistical machine learning, kernel methods, nonparametric statistics
Yee Whye Teh
Bayesian nonparametrics, probabilistic learning, deep learning
Postdocs
Benjamin BloemReddy
Bayesian nonparametrics, probabilistic modeling and inference
Yunlong Jiao
Machine Learning, Kernel Methods, Sparsity Regularization, Computational Genomics
Juho Lee
Bayesian nonparametric models, random graphs
Tom Rainforth
Machine learning, Bayesian inference, Probabilistic programming, Deep generative models
ChiehHsi (Jessie) Wu
Computational statistics, machine learning, stratified medicine.
Graduate Students
Moustafa Abdalla
Multiview Learning, Timeseries modelling, Statistical Genetics, Drug Development, Highthroughput screening
Fadhel Ayed
Bayesian nonparametrics, Machine Learning
Anthony Caterini
HighDimensional Statistics, Monte Carlo Methods, Variational Inference
Sam Davenport
Gaussian Processes, fMRI data, Resampling methods, Random Field Theory
Giuseppe Di Benedetto
Bayesian nonparametrics, Machine Learning
Emilien Dupont
Deep Learning, Generative Models
Adam Foster
Probabilistic inference, optimal experiment design, deep learning, probabilistic programming
Adam Goliński
Probabilistic Inference, Probablistic Programming
Aidan N. Gomez
Neural networks and deep learning
Bradley GramHansen
Probabilistic Programming, Monte Carlo Methods, Variational Inference, Computational Sustainability, Quantum Information
Frauke Harms
Bayesian nonparametrics, machine learning, stochastic geometry
Leonard Hasenclever
Large scale machine learning, probabilistic inference, deep learning
Hyunjik Kim
Gaussian Processes, probabilistic inference, deep generative models
Adam R. Kosiorek
Approximate Inference, Deep Generative Models
Ho Chung Leon Law
Kernel methods, machine learning
Charline Le Lan
Probabilistic Inference, Deep Learning, Reinforcement Learning
Xiaoyu Lu
Machine learning, Gaussian Process, Bayesian Optimization, Adaptive Importance Sampling
Simon Lyddon
Bayesian statistics, decision theory, computational statistics, machine learning.
Chris J. Maddison
Probabilistic inference, Monte Carlo methods, neural networks, point processes
Kaspar Märtens
Statistical machine learning, probabilistic inference, Gaussian Processes, multiview learning
Emile Mathieu
Probabilistic inference, Deep learning, Generative models
Xenia Miscouridou
Statistical Machine Learning, Deep Generative Models, Bayesian Random Graphs
Jovana Mitrovic
Kernel methods, deep learning
Valerio Perrone
Bayesian nonparametrics, deep learning
Emilia Pompe
MCMC methods, Bayesian statistics
Dominic Richards
Optimisation, Monte Carlo Methods
Tim G. J. Rudner
Probabilistic inference, reinforcement learning, Gaussian Processes
Sebastian Schmon
Probabilistic inference, Computational Statistics, Markov chain Monte Carlo, HighDimensional Statistics
Joost van Amersfoort
Machine Learning, Variational Inference, Neural Networks
Stefan Webb
deep learning, deep generative models, probabilistic inference, neural network verification
Matthew Willetts
Large scale machine learning, Bayesian methods, VAEs, Time series segmentation and classification
Jin Xu
Probabilistic inference, deep generative models, representation learning
Qinyi Zhang
Statistical Machine Learning, Kernel Method, Nonparametric Association Measures