Current Projects

🚧 This page is currently under construction. In the near future, I will share details about my research interests, ongoing projects, and how I approach research and teaching. For a small overview of ongoing projects see below.

A large part of my research focuses on analyzing when and why certain modeling approaches succeed or fail, developing assumptions about performance, and testing whether the conditions under study provide the strongest possible insights.

My current projects span several areas:

🤖 Artificial Intelligence, Machine Learning, and Software Development

Adaptive algorithms and automatization using machine learning
Nonlinear effects and machine learning for causal inference
The use of AI in research and teaching
R-package programming and open-source software development

📊 Statistical and Computational Methods

(Nonlinear) Structural Equation Modeling (SEM)
Discrete and Continuous Time Modeling (CTSEM, DSEM)
Generalized Linear Mixed-Effects Models & Hierarchical Data Analysis
Non-parametric estimation with and without measurement noise
Multivariate modeling of long memory in time series

🔍 Power Analysis and Model Evaluation

Simulation-based power analysis
Model fit and model selection
Model and distributional assumptions and the impact of misspecification

🧩 Causality, Heterogeneity, and Psychometrics

Approaches to model causal effects in longitudinal data
Approaches for modeling confounding
Parameter heterogeneity (IPC regression, MNLFA)
(Psychometric) network analysis
Selection effects
Approaches to meta-analysis (meta-SEM, heterogeneity in effect sizes)

🌐 Applications and Broader Interests

Stress at work and emotional labor strategies (deep acting, surface acting) in the framework of job demands and resources
Mental health and well-being
Applications of statistical modeling and methods in:
- Psychology
- Medicine
- Biology
- Epidemiology
- Econometrics

✉️ If you are interested in collaborations or consultations, please feel free to reach out!