Mark van der Wilk

Mark van der Wilk

Senior Lecturer (Associate Professor)
in Machine Learning

Imperial College London


I am a Senior Lecturer (Associate Professor) in the Department of Computing at Imperial College London, researching machine learning. Together with my research group, I work on three central questions:

  • How do we find general patterns that allow generalization beyond the training set? (Equivariance, causality, continual learning…)
  • How can we create neurons that automatically assemble their connectivity structure (architecture), while minimising the computational costs of the network as a whole? (Generalisation bounds, Bayesian model selection, MDL, meta-learning)
  • How do we interact with the environment, while avoiding risk but learning as quickly as possible? (Bayesian optimisation, foundation models for industrial applications e.g. chemistry)

These improvements are relevant for both small-data statistics (Gaussian processes), and large-data machine learning (neural networks). A wide range of research topics contribute towards these improvements, such as invariance, Bayesian inference, causality, meta-learning, local learning rules and generative modelling. My work has been presented at the leading machine learning conferences (e.g. NeurIPS and ICML), and includes a best paper award.

See my Research Overview page for more details on my research interests.

my alt text
Figure: Learning rotationally equivariant filter structure starting from a fully-connedcted structure. Note how the rotation of the filters increases as it learns the equivariant structure [paper].

Working with me

I will have spaces for a small number (~2) of PhD students a year in the next few years. I am looking for people with a strong academic background (particularly strong mathematical skills) who are keen to work on topics that are aligned with my interests. I have written up some tips and guidelines for applying, which I recommend you read before getting in touch or submitting your application.

Academic or Industrial Collaborations

I am also interested in applied problems, and am keen to collaborate. While my research overview gives a more complete picture of topics, I wanted to give a special mention to problems where 1) signal needs to be distinguished from noise, 2) knowledge needs to be encoded into the model, or 3) data is scarce, or needs to be acquired intelligently. Tools like (deep) Gaussian processes can make a difference here, and recent developments have provided new capabilities of dealing with higher-dimensional inputs or large datasets. Current ongoing collaborations include tailored Bayesian optimisation models for biomolecular design or optimisation of chemical processes.

If you have a problem that fits these descriptions, please do get in touch. Collaborations can range from publishing case-studies or datasets which can serve as a community benchmark, to consulting, to methods research.


Before starting at Imperial, I worked with Dr James Hensman for two years as a machine learning researcher at Secondmind, a research-led startup aiming to solve a wide variety of decision making problems. I did my PhD in the Machine Learning Group at the University of Cambridge, working with Prof. Carl Rasmussen, and completing my thesis in 2017. I was funded by the EPSRC and awarded a Qualcomm Innovation Fellowship for my final year. During my PhD, I occasionally worked as a machine learning consultant, and I also spent a few months as a visiting researcher at Google in Mountain View, CA. I moved to the UK from the Netherlands for my undergraduate degree in Engineering.

Recent & Upcoming Talks

Recent Publications

(2022). Actually Sparse Variational Gaussian Processes. NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems.

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(2022). Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. Advances in Neural Information Processing Systems (NeurIPS).


(2022). Memory Safe Computations with XLA Compiler. Advances in Neural Information Processing Systems (NeurIPS).


(2022). Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems.

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(2022). Relaxing Equivariance Constraints with Non-stationary Continuous Filters. Advances in Neural Information Processing Systems (NeurIPS).