Cluster of Excellence “Machine Learning”
In general, I am interested in finding guarantees for and limitations of machine learning algorithms, as well as suitable inductive biases in the climate science context.
For my current project, I study pitfalls and possible improvements in climate network construction.
- High-dimensional Probability
- Ensemble Methods
- Calibrated Uncertainty Quantification
- Graph Neural Networks
- Representation Learning
- Bayesian Inference
- Distribution Shifts
- Climate Data Analysis
I studied Mathematics at the University of Heidelberg with a focus on Mathematical Statistics and Machine Learning. In my master thesis I analyized the convergence rates of Wasserstein GANs with ReLU networks. In May 2021, I started my PhD under joint supervision of Ulrike von Luxburg in the Theory of Machine Learning group (TML) at Tuebingen university, computer science department, and Bedartha Goswami from the MLCS group. I am a scholar in the International Max Planck Research School for Intelligent Systems (IMPRS-IS), a graduate school for PhD students from both university and Max-Planck-Institute in Tuebingen and Stuttgart.