
Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking
ICML 2026
We introduce Kalman Linear Attention, which formulates sequence modelling as a Bayesian filtering problem.
I am a Senior Lecturer (Associate Professor) at the School of Engineering, University of Edinburgh. I co-lead the Bayesian and Neural Systems research group.
My research interests in machine learning include:
I have an MEng in Engineering Science and a DPhil (PhD), both from the University of Oxford. My DPhil was on “Visual recognition in Art using Machine Learning” with Andrew Zisserman in the VGG group. After my DPhil, I was a postdoc at the School of Informatics in Edinburgh with Amos Storkey.
I hold an EPSRC New Investigator Award. I am an investigator on the dAIEdge Horizon Network and a co-investigator on the EPSRC AI Hub for Causality in Healthcare AI.
In addition to research, I am Programme Manager for Electronics and Electrical Engineering at Edinburgh, and I have developed a machine learning course.

ICML 2026
We introduce Kalman Linear Attention, which formulates sequence modelling as a Bayesian filtering problem.

AutoML 2025Code
We accelerate NAS in expressive search spaces by using transferrable performance prediction methods.

NeurIPS 2024Code
A new expressive search space for neural architecture search based on a probabilistic context-free grammar.

BMVC 2024Code
A simple non-hierarchical state space model designed for general visual recognition.