Group Research Interests
My group works very broadly on machine learning and applications of machine learning in the physical sciences and astronomy. To help get a sense of current research interests, you can see my scholar page.
My driving motivation is to automate research in the physical sciences, and I’m interested in a wide range of problems that can help us get there.
Opportunities for Students
There are many opportunities for students in this area of research at the University of Cambridge, which I outline below.
(Do keep in mind that admission to these programs is highly competitive.)
If you are an undergraduate student, I would recommend applying to the new and first-of-its-kind MPhil in Data Intensive Science, which I view as the perfect course for seeding the next generation of AI-for-science researchers. I have the pleasure of teaching the deep learning module for this course, as well as mentoring students and their projects.
You may also consider the MASt in Astronomy which is a more general astronomy degree (I help supervise some projects for).
Note that as a student in any of these programs, you may take lectures across the university.
I can supervise or co-supervise students in both DAMTP and the Institute of Astronomy (I am 50/50 between the two), and am open to co-supervising students who are admitted to PhD programs in other departments, such as Computer Science or Engineering.
If you are interested in joining my group, I have collected some information below that may be helpful.
- PhD in Astronomy
- This is good option for students with interests/experience in astronomy/astrophysics
- PhD in Applied Mathematics and Theoretical Physics (DAMTP)
- This is a good option for students wanting to work with me outside of astronomy, such as entirely in pure ML, or ML applied to non-astro areas of the physical sciences.
- You should apply to the “General Relativity and Cosmology” group, and note their deadline. However, note that this name is just a formality in the department and is not descriptive of my area of research! Thus, the usual specific academic requirements of this group do not apply to students wanting to work with me and I do not require prospective students to take a written test. That being said, I will take into account the Part III results of students who are enrolled the Part III course.
Note that you are also allowed to apply to both programs if you are unsure which is the best fit.
At Cambridge, while funding is automatically considered for many sources as part of the application, I strongly encourage students to apply for external funding as well. I list some funding opportunities and advice below:
- **General advice**
- Data-intensive Science PhD scholarships
- Gates scholarships
- Harding scholarships
- DeepMind scholarships
- NERC scholarships (environmental/climate science/fluids)
- Other scholarships are often available through one’s home country
Internal studentships and many college (what is a college?) scholarships are automatically considered as part of the application.
I have applied and pure machine learning projects available in the following areas (but am of course always open to student-proposed ideas!)
- Machine Learning
- Polymathic AI projects
- Coarsening within foundation models
- Multi-modal embeddings
- Model discovery within foundation models
- Transfering large langugage model concepts to science
- Mathematical formalisation
- Language models for astronomy
- Specific applications of foundation models
- Graph neural networks projects: coarsening, large models, and more
- Symbolic regression fundamental research
- PySR next generation
- Reinforcement learning in the loop
- Research in evolutionary algorithms
- Bayesian approaches and uncertainty estimates
- Tensor expressions (How do we discover GR from data?)
- Co-evolving neural networks with symbolic representations
- i.e., improving on Discovering Symbolic Models from Deep Learning with Inductive Biases
- Gradient expressions and partial differential equations
- Shared sub-expressions
- Applied automatic discovery projects
- Evolving new concepts and empirical relations in astrophysics and fluid dynamics
- Evolving new operators in physics
- Evolving better algorithms for machine learning
- More approaches to incorporating physics into machine learning
- Lagrangian Neural Networks
- The intersection of the renormalization group and machine learning
- Conformal prediction projects
- Polymathic AI projects
- Specific science applications
- Simulation-based inference projects
- Planetary dynamics projects
- Cosmology projects
- Galaxy formation projects
- Multi-scale physics and turbulence projects
- Astrostatistics and survey science projects
I am happy to co-supervise applied machine learning projects with faculty in the Institute of Astronomy and DAMTP in a variety of areas in the physical sciences, so you might consider specific research areas of other faculty which might be interesting to apply machine learning to.