About

I'm a researcher working on statistical machine learning and artificial intelligence. My work, which has twice won best-paper-type awards at top machine learning conferences, focuses on how to learn efficiently from data, and how to automatically gather data. The brain evolved to process information efficiently: therefore, so should our algorithms.

Research Expertise and Current Interests

Data-efficient learning. Gaussian processes, physically structured neural networks, symmetries and inductive biases, non-Euclidean learning.

Where to gather data? Bayesian optimization, Bayesian interactive decision-making, planning under uncertainty, reinforcement learning.

Research

Publications

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels


NeurIPS 2021

Pathwise Conditioning of Gaussian Processes


JMLR 2021

Matérn Gaussian Processes on Graphs


AISTATS 2021

Matérn Gaussian Processes on Riemannian Manifolds


NeurIPS 2020

Efficiently Sampling Functions from Gaussian Process Posteriors


ICML 2020

Aligning Time Series on Incomparable Spaces


AISTATS 2021