Working with me

Working with me for a PhD

Update Apr 2022: all positions filled, if interested, come back at around Oct 2022 for Oct 2023 entry info.

Before contacting me, think about whether you should (not) do a PhD in machine learning, read about my research topics, and think about whether we are a good match.

If you want to do a PhD with me then, I expect you to have a solid skill set in both math and coding. This might be demonstrated by a 1st-class UK degree (or equivalent) in math, physics, computer science or related subjects. I also provide a self-assessment here if you want a bit more reassurance. Publication is a plus factor but not necessary, as long as you can demonstrate your potential for research innovations.

When you contact me, attach your CV, plus a short statement (1 page maximum) about the potential ML research problems you want to work on with me and your expectations about PhD research. Also see the PhD application guideline for submitting an application to Imperial Computing.

Working with me as an MRes student

Update Apr 2022: all positions filled, if interested, come back at around Oct 2022 for Oct 2023 entry info.

Imperial Computing also has an MRes in AI & ML degree program where I am one of the supervisors there. This is a research master program where the degree will be awarded based on finishing a research thesis. Depending on the research results and interview, MRes students can continue for a PhD in AI & ML, or join AI industry for ML engineering roles.

Normally I expect to get 1-3 MRes students per year on projects proposed by myself and/or other faculty members in collaboration (i.e., you might get co-supervisors). I treat MRes students in almost the same way as 1st-year PhD students (the only difference is that they need to finish in 1 year), and I expect them to participate in the group activities of my research group. 

Please see the degree program website for application info. You need to pass an online test by the admission committee before contacting potential supervisors. If you contact me then, please attach your CV, plus a short paragraph describing which project(s) of mine interests you, why, and your relevant skills (see a self-assessment here for skillset). Project titles will be available on the degree program website, and I will send you a detailed description upon contact. Selected candidates will go through an interview by myself and co-supervisors, and we will make acceptance decisions based on the interview results.

Areas for PhD/MRes research topics

We can be a good match if you want to research on the following topics:

  1. Bayesian computation, e.g. approximate inference, sampling;
  2. Uncertainty quantification & decision theory;
  3. Training generative models for vision & NLP;
  4. Sequential generative models & stochastic dynamical models;
  5. Analysing behaviours of (stochastic) neural networks;
  6. Hierarchical/compositional representations, disentangled representations, generating counterfactuals, etc;
  7. Robust ML, e.g. security & privacy issues, model diagnosis/repair;
  8. Adaptive learning, transfer learning & continual learning;
  9. Personalised few-shot learning with e.g. knowledge representations;
  10. Others, e.g. energy-based models, gradient estimation, Stein's method, etc.

Short-term projects/internships/participation

If you are an Imperial undergrad/master/PhD student, contact me for potential projects. 

We will also host "crash courses" on advanced ML topics presented by our team members at the CSML reading group organised by people in Computing & Statistics departments, so reach out if you would like to participate. 

For other people, usually I do not offer paid short-term projects or internships. Still you are welcome to contact me for lightweight discussions and mentorships on related research topics. 

Collaborations

See here for a list of topics that we can potentially collaborate.