Seth Nabarro
I am a machine learning PhD student at Imperial College London, supervised by Mark van der Wilk and Andrew Davison.
I studied physics at Imperial for my undergrad degree, and did a master’s in computational statistics and machine learning at University College London. Before starting my PhD I was a research engineer at Graphcore, looking at hardware acceleration of probabilistic machine learning. More recently, I spent some time as a research intern at Google DeepMind.
Among other things, I am interested in local learning rules for deep learning, continual learning, multi-robot learning, and generative modelling.
news
| Feb 01, 2026 | Our paper A Distributed Gaussian Process Model for Multi-Robot Mapping was accepted at ICRA 2026! |
|---|---|
| Nov 04, 2025 | Submitted my thesis, Towards Deep and Distributed Bayesian Learning! |
| Jan 05, 2025 | Did an internship at Google DeepMind in Zürich August to December 2024, with Mark Collier, Shawn Wang and Efi Kokiopoulou. |
| Aug 01, 2024 | Presented Learning in Deep Factor Graphs with Gaussian Belief Propagation at ICML in Vienna. |
| Nov 07, 2023 | Presented some recent papers on forward gradients (Baydin et al., 2022, Ren et al., 2023) at reading group (slides). |
| Jun 30, 2023 | Won Best Poster for Learning in deep factor graphs with Gaussian belief propagation at the Imperial College Computing Summer Conference, 2023. |
| May 26, 2023 | Chosen as a Top Reviewer for UAI 2023. |
| Aug 05, 2022 | Presented Data augmentation in Bayesian neural networks and the cold posterior effect at UAI in Eindhoven. |