Welcome! I’m Felix Zimmer, an Independent Researcher exploring the intersection of psychology and machine learning. I received my doctoral degree from the University of Zurich, where I specialized in applying machine learning to statistical methodology under the supervision of Dr. Rudolf Debelak.

My research explores innovations at the intersection of empirical research and machine learning. Recently, I’ve been fascinated by leveraging sparsity to build models that are both computationally efficient and maintain strong predictive performance. My current project uses dynamic sparsity to enhance feature selection and interpretability in neural networks.

Outside of research, I enjoy cycling 🚴, bouldering 🧗, and eating lots of Korean ramen 🍜.

Recent updates

• 05/2025. I’ve teamed up with great collaborators for my project on using sparsity for feature selection, which has resulted in a significant update to the preprint.
• 01/2025. We received a research award to develop software for sample size calculations for clinical prediction models.
• 10/2024. My paper exploring sparsity-based feature selection in neural networks is now available as a preprint.
• 12/2023. We developed a new framework for optimizing complex study designs to achieve desired statistical power at minimal costs. Our paper is published in Psychological Methods.
• 11/2023. We provide a tutorial for the mlpwr package to optimize complex study designs (Behavior Research Methods, CRAN).
• 10/2023. We propose new formulas for calculating the necessary sample size when using IRT models. Our paper is published in Psychometrika and our package irtpwr is available on CRAN.
• 04/2023. Successfully defended my PhD thesis in which I developed new methods for power analysis and sample size planning.