Study with me for a PhD
In the next academic year (2021/2022) I am looking for 1-3 PhD students to join my group. Positions filled, if you'd like, come back in ~Oct 2021.
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. 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.
Areas for PhD research topics
We can be a good match if you want to research on the following topics:
- Bayesian computation, including approximate inference and sampling;
- Uncertainty quantification in various applications;
- Generative modelling, for vision and NLP applications;
- Representation learning, especially hierarchical/compositional representations, disentangled/causally consistent representations for generating counterfactuals, etc.
- Robust ML, e.g. security & privacy issues, model diagnosis/repair.
- Personalised few-shot learning with e.g. knowledge representations.
- Others, e.g. transfer learning, continual/life-long learning, energy-based models, gradient estimation, Stein's method, etc.
Short-term projects/internships
If you are an Imperial undergrad/master/PhD student, contact me for potential projects.
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.