Methods Center

Dr. Pascal Kilian

Research Scientist at the Methods Center

Office
Methods Center
Haußerstr. 11
72076 Tübingen
 

Research

Main Research Interests

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)

Curriculum Vitae

since 12/2018
Postdoc at the Methods Center

University of Tübingen

Doctoral Award for Excellent PhD Thesis

by the University of Tübingen (Tübingen School of Education)

07/2018
Promotion (Dr. rer. nat.)

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

03/2018 - 08/2018
Research Scientist (Doctoral Candidate)

at the Methods Center, University of Tübingen

03/2017 - 08/2018
Project "SAM"

Sub-Project Manager

Funded by the Federal Ministry of Education and Research (BMBF)

10/2014 - 04/2017
Project "Entwicklungsverbund 3"

at the Deutsche Telekom Stiftung (Project leadership)

01/2014 - 03/2018
Research Scientist (Doctoral Candidate)

at the Department of Mathematics, University of Tübingen

10/2009 - 12/2013
Research Assistant

at the Department of Mathematics, University of Tübingen

10/2007 - 10/2012
Studies in Mathematics and Physics

at the University of Tübingen

Publications

Selected journal articles

  • Kilian, P., Leyhr, D., Höner, O., & Kelava, A. (submitted). 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.
  • 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