Please send all enquiries via email (with CV) using the tag
[Phd-Enquiry] in the subject, along with either
[<project title>] or [
General] depending on whether you're applying to work on a specified project below or otherwise.
In reaching out to me, it would be quite helpful if you explain (briefly)
- why you want to work with me specifically,
- what you've worked on,
- what you'd be interested in exploring going forward, and
- how that would fit in with what I've been working on.
Outside of the potential opportunities listed below, there are a range of avenues for pursuing a PhD, including through the School of Informatics, or the Centres for Doctoral Training (CDTs) in Data Science, Natural Language Procesing (NLP), Robotics, and Biomedical AI.
Please refer to the information on these pages to learn about the application process, timescales, and funding options.
For prospective Masters students, please see the School's MSc by Research page for details on applying.
If you're already an Informatics MSc student looking for a thesis project, send me an email using the tag [
MSc-Project] in the subject.
I currently have the following open positions for PhD students. Please note eligibility constraints.
Interpretable Representation Learning through Embodied Constraints
Funding: School of Informatics
Start: May/Sept 2023
The focus here is fairly broad, with a nominal goal to leverage data from multiple modalities, such as vision, language, and proprioception, to learn representations in deep latent-variable models that faithfully capture the conceptual commonalities across modalities, for example, what a kite is, or what it means to have wings, without explicit supervision, while retaining the abilities typically expressed within individual domains---interventions and generations. The idea is to reason about the characteristic latent features derived from such models---to identify interpretable components, capture notions of 'relatedness', along with broad dependency structure reflecting the nature of collective data observed.