Dr. Pascal Kilian
Research Scientist at the Methods Center
Office
Methods Center
Haußerstr. 11
72076 Tübingen
+49 7071 29 74694
Fax: +49 7071 29 35264
pascal.kilian @uni-tuebingen.de
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