Group

KIPS (Kernels and Information Processing Systems) 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.

In Adelaide we are based with the Australian Institute for Machine Learning (AIML) and with the Statistics group in the School of Computer and Mathematical Sciences. Members based in Oxford are a part of the Computational Statistics and Machine Learning (OxCSML).

We will have several openings for Postdocs and PhD students in 2025 to be based in Adelaide and are looking for candidates with a strong machine learning, statistical or mathematical background – please get in touch if you are interested!

Postdocs (Adelaide)

Gaurangi Anand
graph neural networks, link and relation prediction, time series
Erdun Gao
reliable decision making, causality

HDR Students (Adelaide)

Vinh Nguyen
causality, reinforcement learning
Peter Moskvichev
uncertainty calibration, deep learning
Vivienne Niejalke
geolocation, covariance modelling

DPhil Students (Oxford)

Jake Fawkes
causality, fairness, domain generalisation
Veit Wild
Bayesian nonparametrics, Gaussian processes, variational inference

Alumni

Jovana Mitrovic, DPhil 2019, Oxford, Thesis: Representation Learning with Kernel Methods (now Senior Research Scientist at Google 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)
Valerie Bradley, DPhil 2024, Oxford, Thesis: Quantifying and mitigating selection bias in probability and nonprobability samples (now Director of Analytics Polling at Harris for President)
Shahine Bouabid, DPhil 2024, Oxford, Thesis: Transforming kernel-based learners to incorporate domain knowledge from climate science (now Postdoc at MIT)

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