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Computational Statistics
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Machine Learning
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Statistical Methodology
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Statistical Theory
Computational Statistics
Machine Learning
Statistical Methodology
Statistical Theory
Faculty
![George Deligiannidis](/img/people/male.png)
George Deligiannidis
Computational Statistics, Monte Carlo methods
![Arnaud Doucet](/img/people/doucet.jpg)
Arnaud Doucet
Computational Statistics, Monte Carlo methods
![Geoff Nicholls](/img/people/nichollsPB.jpg)
Geoff Nicholls
Statistical modeling, Bayes Methods, Monte Carlo Methods.
![Tom Rainforth](/img/people/rainforth.jpeg)
Tom Rainforth
Machine learning, Bayesian inference, Probabilistic programming, Deep generative models
Post-docs
![M. Azim Ansari](/img/people/male.png)
M. Azim Ansari
Statistical Genetics, Evolution, Host Pathogen Interactions, Computational Biostatistics, Machine Learning, Bayesian Statistics
![Emile Mathieu](/img/people/mathieu.jpg)
Emile Mathieu
Probabilistic inference, Deep learning, Generative models, Representation Learning, Geometry
![George Nicholson](/img/people/nicholson.jpg)
George Nicholson
Computational biostatistics, machine learning, precision medicine
Graduate Students
![Shahine Bouabid](/img/people/bouabid.jpg)
Shahine Bouabid
Kernel Methods, Gaussian processes, Climate emulation
![Anthony Caterini](/img/people/caterini.jpg)
Anthony Caterini
High-Dimensional Statistics, Monte Carlo Methods, Variational Inference
![Sam Davenport](/img/people/davenport.png)
Sam Davenport
Gaussian Processes, fMRI data, Resampling methods, Random Field Theory
![Fabian Falck](/img/people/falck.jpg)
Fabian Falck
Probabilistic Deep Learning, Deep Generative Models, Causality, Applications in Health
![Tyler Farghly](/img/people/farghly.jpg)
Tyler Farghly
Learning theory, Optimisation, Monte Carlo methods
![Edwin Fong](/img/people/fong.png)
Edwin Fong
Bayesian inference under model misspecification, Bayesian nonparametrics
![Adam Foster](/img/people/foster.jpg)
Adam Foster
Probabilistic machine learning, deep learning, unsupervised representation learning, optimal experimental design, probabilistic programming
![Frauke Harms](/img/people/harms.jpg)
Frauke Harms
combinatorics, computational complexity, Bayesian nonparametrics, machine learning, stochastic geometry
![Bobby He](/img/people/he.jpg)
Bobby He
Machine learning, deep learning, uncertainty quantification
![Desi R. Ivanova](/img/people/ivanova.jpg)
Desi R. Ivanova
Bayesian Inference, Statistical Machine Learning, Optimal Experimental Design
![Jannik Kossen](/img/people/kossen.jpg)
Jannik Kossen
Active Learning, Bayesian Deep Learning, Transformers
![Francesca Panero](/img/people/panero.jpg)
Francesca Panero
Bayesian random graphs, Bayesian nonparametrics, disclosure risk
![Emilia Pompe](/img/people/pompe.png)
Emilia Pompe
MCMC methods, Bayesian statistics
![Tim Reichelt](/img/people/reichelt.jpg)
Tim Reichelt
Probabilistic Programming, Probabilistic Inference
![Tim G. J. Rudner](/img/people/rudner.jpg)
Tim G. J. Rudner
Probabilistic inference, reinforcement learning, Gaussian Processes
![Jean-Francois Ton](/img/people/ton-photo_1.jpeg)
Jean-Francois Ton
Kernel methods, Meta-learning
![Hanwen Xing](/img/people/male.png)
Hanwen Xing
Computational methods, Bayesian inference
![Schyan Zafar](/img/people/schyanzafar_bw.jpg)
Schyan Zafar
Monte Carlo methods, Multivariate stochastic processes
Alumni
- Louis Aslett
- Marco Battiston
- Benjamin Bloem-Reddy
- Ryan Christ
- Bradley Gram-Hansen
- Leonard Hasenclever
- Ho Chung Leon Law
- Juho Lee
- Zhu Li
- Thibaut Lienart
- Xiaoyu Lu
- Simon Lyddon
- Chris J. Maddison
- Kaspar Märtens
- Xenia Miscouridou
- Valerio Perrone
- Dominic Richards
- Andrew Roth
- Patrick Rubin-Delanchy
- Sebastian Schmon
- Stefan Webb
- Matthew Willetts
- Chieh-Hsi (Jessie) Wu
- Yuan Zhou