Julian Karch is Assistant Professor (UD) at the unit Methodology & Statistics of the Institute of Psychology at Leiden University. His main research interests are the adaptation and application of statistical learning methods to the challenges of psychological data analysis. He teaches statistical courses for the psychological masters and the statistical science master.
Julian obtained his Diploma in Computer Science with a focus on statistical learning and data science in 2012 from the Free University of Berlin. He then proceeded to do his PhD in Quantitative Psychology at the Max Planck Institute for Human Development in Berlin. During this time, he was also an adjunct researcher of the Methodology unit of the Institute of Psychology at the Humboldt University of Berlin. In 2016, he defended his PhD thesis (summa cum laude) entitled “A Machine Learning Perspective on Repeated Measures: Gaussian Process Panel and Person-Specific EEG Modeling”. Afterwards, he continued as a postdoctoral researcher at the Max Planck Institute for Human Development. As part of his master and PhD theses, Julian also worked at the Quantitative Psychology unit of the University of Virginia and the Wellcome Trust Centre for Neuroimaging at the University College London.
- Best Research Proposal Award, MPS/UCL Symposium on Computational Psychiatry and Aging, 2016
- Karch J.D. (2021), Psychologists Should Use Brunner-Munzel’s Instead of Mann-Whitney’s U Test as the Default Nonparametric Procedure, Advances in Methods and Practices in Psychological Science .
- Karch J.D. (2020), Improving on adjusted R-squared, Collabra: Psychology 6(1): 45.
- Karch J.D., Brandmaier A.M. & Voelkle M.C. (2020), Gaussian process panel modeling—machine learning inspired analysis of longitudinal panel data, Frontiers in Psychology 11: 351.
- Kloos K., Meertens Q., Scholtus S. & Karch J.D. (2020), Comparing Correction Methods to Reduce Misclassification Bias. In: BNAIC/BENELEARN 2020 proceedings..
- Karch J.D., Fivelich E., Wenger E., Lisofski N., Becker M., Butler O., Martenson J., Lindenberger U.L., Brandmaier A.M. & Kühn S. (2019), Identifying predictors of within-person variance in MRI-based brain volume estimates, NeuroImage 200: 575-589.
- Karch J.D., Sander M.C., Von Oertzen T., Brandmaier A.M. & Werkle-Bergner M. (2015), Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance, NEUROIMAGE 118: 538-552.
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