
Computational Statistics

Machine Learning

Statistical Methodology
Computational Statistics
Machine Learning
Statistical Methodology
The group in numbers:
 NIPS papers: 42
 NIPS orals: 7
 ICML papers: 15
 UAI papers: 14
 AISTATS papers: 9
 JMLR papers: 5
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
Dino Sejdinovic
Statistical machine learning, kernel methods, nonparametric statistics
Yee Whye Teh
Bayesian nonparametrics, probabilistic learning, deep learning
Postdocs
Marco Battiston
Bayesian nonparametrics
Seth Flaxman
Scalable spatiotemporal statistics and Bayesian machine learning for public policy and social science
Konstantina Palla
nonparametric Bayesian methods and models
ChiehHsi (Jessie) Wu
Computational statistics, machine learning, stratified medicine.
Graduate Students
Moustafa Abdalla
Multiview Learning, Timeseries modelling
Giuseppe Di Benedetto
Bayesian nonparametrics, Machine Learning
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
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
Emile Mathieu
Bayesian nonparametrics, probabilistic inference, deep learning
Xenia Miscouridou
Bayesian nonparametrics, Machine Learning, Networks
Jovana Mitrovic
Kernel methods, deep learning
Valerio Perrone
Bayesian nonparametrics, deep learning
Stefan Webb
deep learning, deep generative models, probabilistic inference
Matthew Willetts
Large scale machine learning, Bayesian methods, Gaussian processes, Time series segmentation and classification
Qinyi Zhang
Statistical Machine Learning, Kernel Method, Nonparametric Association Measures