I am an Associate Professor in Statistics at the University of Oxford, a Fellow of Mansfield College, an Associate Member of the Oxford-Man Institute, and a Faculty Fellow of the Alan Turing Institute. I conduct research at the interface between machine learning and statistics, with occasional excursions to communications engineering and neuroscience.

Statistical machine learning, reproducing kernel Hilbert spaces, hypothesis testing with big data, measures of association and multivariate interaction, tradeoffs between computational and statistical efficiency, coding and information theory.

**May 2016.** New papers:

**DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression**with J. Mitrovic and Y.W. Teh accepted to ICML 2016.**Bayesian Learning of Kernel Embeddings**with S. Flaxman, J.P. Cunningham and S. Filippi accepted to UAI 2016 as a plenary presentation.**Random Features for Online Sampling with a Reservoir**(preprint to follow soon) with B. Paige and F. Wood accepted to UAI 2016.**Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings**(preprint to follow soon) with G. Franchi and J. Angulo accepted to IEEE ICIP 2016.

**March 2016.** Visiting the Institute of Statistical Mathematics : slides for my talk on kernel embeddings for inference with intractable likelihoods.

**December 22, 2015.** Paper K2-ABC: Approximate Bayesian Computation with Kernel Embeddings with M. Park and W. Jitkrittum accepted to AISTATS 2016, as a full oral presentation (6.5% of submissions).

**October 13, 2015.** Paper **Kernel Sequential Monte Carlo** with I. Schuster, H. Strathmann and B. Paige is now on arXiv.

**September 28-30, 2015.** ATI Scoping Workshop on *Statistical and Computational Challenges in Large-Scale Data Analysis*.

**September 4, 2015.** Two papers accepted to NIPS 2015:

- Fast Two-Sample Testing with Analytic Representations of Probability Measures with K. Chwialkowski, A. Ramdas and A. Gretton
- Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families with H. Strathmann, S. Livingstone, Z. Szabo and A. Gretton.

**July 11, 2015.** Co-organising workshop Large-Scale Kernel Learning (LSKL) colocated with ICML 2015. We had a fantastic lineup of speakers including Zaid Harchaoui, Marius Kloft, Neil Lawrence, Francis Bach and Ruslan Salakhutdinov. Talk details and slides are available here.

**April 7-10, 2015.** Attending Dagstuhl seminar on *Machine Learning with Interdependent and Non-identically Distributed Data*: slides for my talk on hypothesis testing with kernel embeddings on interdependent data.

**February 10, 2015.** Paper **K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings** with M. Park and W. Jitkrittum is now on arXiv.

**January 14, 2015.** Paper **Unbiased Bayes for Big Data: Paths of Partial Posteriors** with H. Strathmann and M. Girolami is now on arXiv.

**January 7-9, 2015.** Attending *UCL Workshop on the Theory of Big Data* : slides for my talk on large-scale hypothesis testing with kernel embeddings.