Group

KIPS (Kernels and Information Processing Systems) OX+OZ (Oxford+Australia) conducts research spanning a variety of topics at the interface between machine learning and statistical methodology, including:

Robust and trustworthy machine learning,
Uncertainty quantification,
Causal reasoning,
Explainability,
Large-scale nonparametric and kernel methods,
Multiresolution data and data across modalities,
Physics-informed models,
Measures of dependence and multivariate interaction,
Hierarchical and deep generative modelling.

Members based in Oxford are a part of the Computational Statistics and Machine Learning (OxCSML) within the Department of Statistics and we closely collaborate with other researchers in OxCSML.

DPhil Students (Oxford)

Shahine Bouabid
kernel methods, Bayesian nonparametrics, deep learning, aerosol-cloud interaction
Valerie Bradley
kernel methods, selection bias
Jake Fawkes
causality, fairness, domain generalisation
Veit Wild
Bayesian nonparametrics, Gaussian processes, variational inference

HDR Students (Adelaide)

Peter Moskvichev
uncertainty calibration, deep learning
Vivienne Niejalke
geolocation, covariance modelling

Alumni

Jovana Mitrovic, DPhil 2019, Oxford, Thesis: Representation Learning with Kernel Methods (now Senior Research Scientist at DeepMind)
Ho Chung Leon Law, DPhil 2019, Oxford, Thesis: Testing and Learning on Distributional and Set Inputs (now Quantitative Researcher at Citadel Securities)
Qinyi Zhang, DPhil 2020, Oxford, Thesis: Kernel Based Hypothesis Tests: Large-Scale Approximations and Bayesian Perspectives (now Quantitative Researcher at Afairi AG)
Zhu Li, DPhil 2021, Oxford, Thesis: On the Properties of Random Feature Methods (now Postdoc at Gatsby Computational Neuroscience Unit, UCL)
David Rindt, DPhil 2021, Oxford, Thesis: Nonparametric Independence Testing and Regression for Time-to-Event Data (now Quantitative Researcher at GSA Capital)
Anthony Caterini, DPhil 2021, Oxford, Thesis: Expanding the Capabilities of Normalizing Flows in Deep Generative Models and Variational Inference (now Machine Learning Scientist at Layer6 AI, Toronto)
Jean-Francois Ton, DPhil 2022, Oxford, Thesis: Causal Reasoning and Meta Learning using Kernel Mean Embeddings (now Senior Research Scientist at TikTok)
Robert Hu, DPhil 2022, Oxford, Thesis: Large Scale Methods for Kernels, Causal Inference and Survival Modelling (now Applied Scientist at Amazon)
Siu Lun Chau, DPhil 2023, Oxford, Thesis: Towards Trustworthy Machine Learning with Kernels (now Postdoc at CISPA Helmholtz Center for Information Security, Saarbrucken)

Visitors

Gianni Franchi, Aug-Nov 2015
Emiliano Diaz Salas Porras, Oct-Dec 2019
Julien Lenhardt, May-Jun 2022
Mengyan Zhang, Jul 2023
Siu Lun Chau, Nov-Dec 2023
Daokun Zhang, Jan 2024