I continually supervise student projects and special courses at all levels. If you are interested in working with me within one of the project themes listed here, you are very welcome to contact me.
Recommended courses
As a prerequisite to project work I recommend the following courses
Approximate Bayesian inference in deep neural networks, using techniques such as stochastic gradient Markov chain Monte Carlo sampling, Kronecker-factored approximate curvature, ensemble methods, stochastic weight averaging, variational inference, etc.
Uncertainty quantification
Methods for quantifying uncertainty including Bayesian methods, conformal prediction, and uncertainty distillation.
Graph neural networks
Applications of graph neural networks to models of molecules and materials.
Sum-product networks/tensor trains
Probabilistic graphical models that allow for exact inference (marginalization, conditioning etc.) with applications to density estimation, variational inference, active learning, and reinforcement learning.