Manuel Haussmann

I just (summer 2021) finished a Ph.D. at Heidelberg University in the group of Fred Hamprecht. I am broadly interested in all things Bayesian and Probabilistic Machine Learning. My interest is both on the foundational level as well as with respect to the growing field of applications.


  • 2021: I successfully passed my defense!
  • 2021: Our preprint on Evidential Turing Processes is now on arXiv
  • 2021: Our preprint on Understanding Event-Generation Network via Uncertainties, a collaboration with the group of Tilman Plehn, is now on arXiv
  • 2021: Our work on Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes has been accepted at AISTATS 2021
  • 2020: Bayesian Evidential Deep Learning with PAC Regularization has been accepted at the 3rd Symposium on Advances in Approximate Bayesian Inference
  • 2020: I was selected as a top reviewer for ICML 2020
  • 2020: Deep-learning jets with uncertainties and more , a collaboration with the group of Tilman Plehn, has been published in SciPost Physics
  • 2019: Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation has been accepted at UAI 2019
  • 2019: Deep Active Learning with Adaptive Acquisition has been accepted at IJCAI 2019
  • 2019: Elke's LeMoNADe: Learned Motif and Neuronal Assembly Detection in Calcium Imaging Videos has been accepted at ICLR 2019
  • 2017: Variational Bayesian Multiple Instance Learning with Gaussian Processes has been accepted at CVPR 2017



See Google Scholar for the current list.