I started as a PhD student at the Statistical Learning & Data Science working group in February 2022.
I obtained a Bachelor's degree (B.A.) in Business Administration from DHBW Ravensburg and a Bachelor's and consecutive Master's degree (B.Sc., M.Sc.) in Statistics from LMU Munich.
My main research focus is on the quantification of uncertainty, where I am active in our research subgroups Probabilistic Machine and Deep Learning and Causal and Fair Machine Learning.
I receive funding from the Konrad Zuse School of Excellence in Reliable AI (relAI).
Institut für Statistik
lisa.wimmer [at] stat.uni-muenchen.de
- Predictive uncertainty
- Probabilistic machine learning
- Bayesian deep learning
- Uncertainty and causal inference
- Imprecise probabilities
- Weak supervision
You can find me on
- Bothmann L, Wimmer L, Charrakh O, Weber T, Edelhoff H, Peters W, Nguyen H, Benjamin C, Menzel A (2023) Automated wildlife image classification: An active learning tool for ecological applications. arXiv preprint arXiv:2303.15823.
- Wiese JG, Wimmer L, Papamarkou T, Bischl B, Günnemann S, Rügamer D (2023) Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. arXiv preprint arXiv:2304.02902.