Fabian Falck

Fabian Falck

Probabilistic Deep Learning, Deep Generative Models, Causality, Applications in Health

I am a third year PhD student in Statistical Machine Learning at University of Oxford, supervised by Prof. Chris Holmes and Prof. Arnaud Doucet. I am also a Graduate Teaching and Research Scholar in Computer Science at Oriel College, University of Oxford, teaching maths courses.

Recently, I have worked on analysing the regularisation property of U-Nets, which are widely used in generative modelling, and hierarchical variational autoencoders (NeurIPS 2022 oral, to appear). I also studied generalising the propensity score theory to balancing scores in matching for treatment effect estimation in causal inference (AISTATS 2022), and variational autoencoders for clustering to find multiple partitions of high-dimensional data (NeurIPS 2021).

I studied Computer Science (MSc) at Imperial College London, and Engineering (BSc+MSc) at Karlsruhe Institute of Technology in Germany. During my degrees, I studied at and visited Tsinghua University (清华大学) in Beijing, Shanghai Jiao Tong University (上海交通大学), the University of Oxford, Singapore Management University, and Carnegie Mellon University in the US.



  • O. Clivio , F. Falck , B. Lehmann , G. Deligiannidis , C. Holmes , Neural score matching for high-dimensional causal inference, in International Conference on Artificial Intelligence and Statistics, 2022, 7076–7110.
  • F. Falck , C. Williams , D. Danks , G. Deligiannidis , C. Yau , C. Holmes , A. Doucet , M. Willetts , A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs, Advances in Neural Information Processing Systems, 2022.


  • F. Falck , H. Zhang , M. Willetts , G. Nicholson , C. Yau , C. Holmes , Multi-Facet Clustering Variational Autoencoders, Advances in Neural Information Processing Systems, 2021.