About Me
Hi I’m Nico Palm. I am a mathematician and currently pursuing my PhD at the Munich Center for Machine learning (LMU) under the supervision of Thomas Nagler. Before that I worked for a couple of startups in the broad field of data analysis and machine learning. I specialize in the intersection of machine learning and statistics/mathematics: I develop statistical/mathematical methods that meet the requirements of modern data science, explore and prove their theoretical properties and package them in user-friendly software.
- Theoretical Statistics
- Uncertainty Quantification in ML
- Category Theory Approaches in ML
- Multi-Objective Optimization
PhD Computational Statistics & Data Science
LMU Munich
MSc Mathematics
University of Regensburg
BSc Mathematics
University of Regensburg
My current focus is on non-stationary time series. These are time series that can show trends and shifts in distribution. Non-stationary time series are difficult to study. As a result, very few general results are known. From a practical point of view, however, data are rarely stationary, which stresses the need for such results. My overall goal is to a) generalize fundamental theorems of probability theory (e.g. central limit theorems) and statistics and b) extend practical algorithms, such as the bootstrap, to the non-stationary context.
I am also interested in:
- The broad area of uncertainty quantification for ML models, mainly from a frequentist perspective
- Category theory in ML
- Multi-objective optimization especially in the context of expensive blackbox functions (e.g. simulation or digital twin models)
- Applying all this to real scenarios
Feel free to reach out!