KIPS
KIPS conducts research spanning a variety of topics at the interface between machine learning and statistical methodology. We are based at the Australian Institute for Machine Learning and at the School of Mathematical Sciences at Adelaide University. Until late 2022, KIPS was part of the Computational Statistics and Machine Learning group (OxCSML) at the University of Oxford.
Join us at FIMI Down Under 2026 on – in Adelaide!
Professor of Statistical Machine Learning
School of Mathematical Sciences & Australian Institute for Machine Learning
Adelaide University
Robust and trustworthy machine learning, causal inference, uncertainty quantification, and large-scale nonparametric and kernel methods.
Postdoctoral researcher
Responsible AI, AI safety, SE4AI
Postdoctoral researcher
Probabilistic machine learning, Bayesian deep learning, functional inference
PhD student
Causality, reinforcement learning
PhD student
Experimental design, causality, fairness
Our research spans a variety of topics at the interface between machine learning and statistical methodology.
ThesisData quality in causal machine learning with applications to algorithmic fairness
Next roleCHAI Research Fellow at University College London
ThesisGeneralized variational inference in infinite dimensions
Next roleQuantitative Researcher at Appian Way Energy Partners
ThesisTransforming kernel-based learners to incorporate domain knowledge from climate science
Next rolePostdoc at MIT
ThesisQuantifying and mitigating selection bias in probability and nonprobability samples
Next roleChief Data Science & Innovation Officer at Impact Research
ThesisTowards trustworthy machine learning with kernels
Next roleAssistant Professor at Nanyang Technological University, Singapore
ThesisLarge scale methods for kernels, causal inference and survival modelling
Next roleResearch Scientist at Graphcore
ThesisCausal Reasoning and Meta Learning using Kernel Mean Embeddings
Next roleSenior Research Scientist at TikTok
ThesisExpanding the Capabilities of Normalizing Flows in Deep Generative Models and Variational Inference
Next roleSenior Machine Learning Scientist at Layer6 AI, Toronto
ThesisNonparametric Independence Testing and Regression for Time-to-Event Data
Next roleQuantitative Researcher at IMC Trading
ThesisOn the Properties of Random Feature Methods
Next roleChapman Fellow at Imperial College London
ThesisKernel Based Hypothesis Tests: Large-Scale Approximations and Bayesian Perspectives
Next roleQuantitative Researcher at Afairi AG
ThesisTesting and Learning on Distributional and Set Inputs
Next roleQuantitative Researcher at Citadel Securities
ThesisRepresentation Learning with Kernel Methods
Next roleSenior Research Scientist at Google DeepMind
PositionPostdoc, 2024–2026, Adelaide
PositionPostdoc, 2024–2026, Adelaide
ThesisKernel Differences Between Conditional Distributions with Applications in Uncertainty Calibration
Next rolePhD student at NTU Singapore
ThesisGeolocation with Latent Variable Models
