Welcome! I’m Felix Zimmer, an Independent Researcher exploring the field of machine learning. Prior to this, I received a doctoral degree from the University of Zurich, where I specialized in psychological methods under the supervision of Dr. Rudolf Debelak.

My research leverages innovative deep learning techniques to enhance model capability and efficiency. One focus is to explore how sparsity can make models much more efficient while retaining powerful predictions. For example, my recent project applies sparsity to improve feature selection.

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

Recent updates

• 01/2025. We received a research award to develop software for sample size calculations for clinical prediction models.
• 10/2024. My first pure ML paper on using sparsity for 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.