I am passionate about the science and application of educational statistics. My expertise spans a range of advanced methodological techniques, including factor analytic models, structural equation models, and various longitudinal data analysis methods, such as latent growth modeling, mixed-effects modeling, and growth mixture modeling. My research is centered on the development and refinement of statistical methodologies for analyzing complex data within education, psychology, and the broader social and behavioral sciences, with a particular focus on longitudinal data—repeated measures collected over time. Through both methodological innovation and interdisciplinary collaboration, I aim to push the boundaries of applied statistics. A key focus of my work is advancing the understanding of (non)linear relationships among observed and latent variables. I specialize in latent variable methods, particularly nonlinear mixed-effects models and their variations, as well as nonlinear structural equation models. My goal is to contribute to the applied statistics literature, by providing researchers and practitioners with robust theoretical foundations and empirical tools to address critical questions in fields such as education, psychology, health, and human development.