Methodenzentrum

Dr. Pascal Kilian

Habilitation Candidate at the Methods Center

Office
Methods Center
Haußerstr. 11
72076 Tübingen


 

Key Research Topics

Applied Research

  • Research in the field of soccer
    • Scientific Support for the DFB TID (Talent Identification and Development) Program
    • Prognostic relevance of talent predictors
    • Game analytics

Method Development

Machine learning in social and behavioral sciences (modeling human behavior) and linking machine learning and psychometrics

  • Latent Variables
    • Auto-Encoders and Variational Auto-Encoders
  • (Multi-level) sequential data
    • Linking Multi-level models and recurrent neural networks (RNNS)

Projects

Profile

since 2023
(Senior) Big Data Analyst / AI Developer

BOSCH

2018 - 2023
Postdoc

Methods Center, University of Tübingen

Doctoral Award for Excellent PhD Thesis

by the University of Tübingen (Tübingen School of Education)

2018
PhD (Dr. rer. nat.)

Department of Mathematics, University of Tübingen (Title: On CK, PCK and Student Dropout in the Early Phase of Math (Teacher) Education at University)

2018
Research Scientist

Methods Center, University of Tübingen

2014 - 2017
Project "Entwicklungsverbund 3"

Deutsche Telekom Stiftung (Project leadership)

2014 - 2018
Research Scientist

Department of Mathematics, University of Tübingen

2009 - 2013
Research Assistant

Department of Mathematics, University of Tübingen

2007 - 2012
Studies in Mathematics and Physics

University of Tübingen

Publications

  • Kilian, P., Leyhr, D., Höner, O., & Kelava, A. (2023). A deep learning factor analysis model based on importance-weighted variational inference and normalizing flow priors: Evaluation within a set of multidimensional performance assessments in youth elite soccer players. Statistical Analysis and Data Mining, 16, 474-487. Link
  • Andriamiarana, V., Kilian, P., Kelava, A., & Brandt, H. (2023). On the requirements of non-linear dynamic latent class SEM: A simulation study with varying numbers of subjects and time points. Structural Equation Modeling. Link
  • Kilian, P., Ye, S., & Kelava, A. (2023). Mixed effects in machine learning - A flexible mixedML framework to add random effects to supervised machine learning regression. Transactions on Machine Learning Research (TMLR). Link
  • Kelava, A., Kilian, P., Glaesser, J., Merk, S., & Brandt, H. (2022). Forecasting intraindividual changes of affective states taking into account interindividual differences using intensive longitudinal data from a university student drop out study in math. Psychometrika, 87(2), 533-558. Link
  • Glaesser, J., Kilian, P., & Kelava, A. (2021). Mögliche Vorläufer von Studienabbruch in der Mathematik: stabile Persönlichkeitsmerkmale und veränderliche affektive Zustände. In M. Neugebauer, H.-D. Daniel, & A. Wolter (Eds.), Studienerfolg und studienabbruch (pp. 127–149). Wiesbaden: Springer Fachmedien Wiesbaden. doi: 10.1007/978-3-658-32892-4_6
  • Kilian, P., Glaesser, J., Loose, F., & Kelava, A. (2021). Structure of pedagogical content knowledge in maths teacher education. Psychological Test and Assessment Modeling, 63, 337–360.
  • Kilian, P., Loose, F., & Kelava, A. (2020). Predicting math student success in the initial phase of college with sparse information using approaches from statistical learning. Frontiers in Education, 5. Link