Christoph Kern
Photo credit: Daniela Haupt
| Course name | Details | Term |
|---|---|---|
| GESS Seminar: Interpretability and Fairness in Machine Learning | Course details | spring term |
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Research Interests
- Machine/ Statistical Learning
- Fair and Interpretable ML
- Topics in Survey Research (Digital Trace Data, Sensor Data, Non-probability Samples)
- Statistics (Structural Equation Modeling)
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Career
Academic Career
Christoph Kern graduated in 2011 (Dipl.-Soz.-Wiss.) from the University of Duisburg-Essen (UDE), where he studied sociology with a focus on methods for empirical social science research. In 2016 he completed his PhD (Dr. rer. pol.) at the UDE with a thesis on „Modeling dyadic decision-making processes of regional mobility“.
Since 2017 Christoph Kern is a Post-Doctoral Researcher at the Professorship for Statistics and Methodology at the University of Mannheim. Before joining the University of Mannheim, he was a Research Associate at the Professorship for Empirical Social Research and Statistics at the University of Duisburg-Essen (2011-2017).
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Publications
- Kuppler, M., Kern, C., Bach, R. L. & Kreuter, F. (2021). Distributive justice and fairness metrics in automated decision-making: How much overlap is there? Ithaca, NY: Cornell University.
- Badillo-Goicoechea, E., Chang, T.-H., Kim, E., LaRocca, S., Morris, K., Deng, X., Chiu, S., Bradford, A., Garcia, A., Kern, C., Cobb, C., Kreuter, F. & Stuart, E. A. (2020). Global trends and predictors of face mask usage during the COVID-19 pandemic. Ithaca, NY: Cornell University.
- Brian, K., Kern, C., Scott, M. J., Hunter, C. & Kumar, A. (2020). Workbooks. In I. Foster, R. Ghani, R. S. Jarmin, F. Kreuter & J. Lane (eds.), Big data and social science : data science methods and tools for research and practice (2., S. 333–340). Milton ; Boca Raton, FL: CRC Press. https://doi.org/10.1201/9780429324383-13
- Erlinghagen, M., Kern, C. & Stein, P. (2020). Migration, social stratification and dynamic effects on subjective well being. Advances in Life Course Research(Article 100393), 1–17. https://doi.org/10.1016/j.alcr.2020.100393
- Gerdon, F., Theil, C. K., Kern, C., Bach, R. L., Kreuter, F., Stuckenschmidt, H. & Eckert, K. (2020). Exploring impacts of artificial intelligence on urban societies with social simulations. 40. Kongress der Deutschen Gesellschaft für Soziologie, Online.
- Kern, C. (2020). Machine learning interpretation tools. In P. Atkinson (eds.), SAGE research methods: Foundations (S. 1–12). London: SAGE Publications. https://doi.org/10.4135/9781526421036913344
- Kern, C., Höhne, J. K., Schlosser, S. & Revilla, M. (2020). Completion conditions and response behavior in smartphone surveys: A prediction approach using acceleration data. Social Science Computer Review : SSCORE, 1–19. https://doi.org/10.1177/0894439320971233
- Kern, C., Höhne, J. K., Schlosser, S. & Revilla, M. (2020). Completion conditions and response behavior in smartphone surveys: A prediction approach using acceleration data. Social Science Computer Review : SSCORE, 1–19. https://doi.org/10.1177/0894439320971233
- Kern, C., Li, Y. & Wang, L. (2020). Boosted kernel weighting – using statistical learning to improve inference from nonprobability samples. Journal of Survey Statistics and Methodology : JSSAM(smaa028), 1–26. https://doi.org/10.1093/jssam/smaa028
- Kolb, J.-P., Weiss, B. & Kern, C. (2020). Using predictive modelling to identify panel nonresponse. In M. U. Mahidin (eds.), Proceeding ISI World Statistics Congress 2019 : Contributed paper session : 18 – 23 August 2019, Kuala Lumpur (S. 206–214). , Department of Statistics Malaysia (DOSM): Putrajaya.
- Bach, R. L., Kern, C., Amaya, A., Keusch, F., Kreuter, F., Hecht, J. & Heinemann, J. (2019). Predicting voting behavior using digital trace data. Social Science Computer Review : SSCORE. https://doi.org/10.1177/0894439319882896
- Erlinghagen, M., Kern, C. & Stein, P. (2019). Internal migration, social stratification and dynamic effects on subjective well being. SOEP papers on Multidisciplinary Panel Data Research, 1046. Berlin: DIW.
- Kern, C., Klausch, T. & Kreuter, F. (2019). Tree-based machine learning methods for survey research. Survey Research Methods : SRM, 13(1), 73–93. https://doi.org/10.18148/srm/2019.v1i1.7395
- Kern, C., Weiss, B. & Kolb, J.-P. (2019). A longitudinal framework for predicting nonresponse in panel surveys. Ithaca, NY: Cornell University.
- Kern, C. & Stein, P. (2018). Modelling decision-making processes of regional mobility in a dyadic framework. European Sociological Review, 34(4), 433–451. https://doi.org/10.1093/esr/jcy012
- Stein, P. & Kern, C. (2018). Konzeption eines dyadischen Modells zur Analyse von Entscheidungsprozessen räumlicher Arbeitsmarktmobilität im Mehrebenenkontext. In S. Quack (eds.), Transnationalisierung der Arbeit (S. 71–95). Wiesbaden: Springer VS. https://doi.org/10.1007/978-3-658-20939-1
- Kern, C. (2017). Dyadische Analyse regionaler Arbeitsmarktmobilität : Modellierung von Entscheidungsprozessen im Mehrebenenkontext . Wiesbaden: Springer VS. https://doi.org/http://doi.org/10.1007/978-3-658-17435-4
- Kern, C. & Stein, P. (2016). Effect comparison in multilevel structural equation models with non-metric outcomes. In JSM Proceedings : papers presented at the Joint Statistical Meetings ... and other ASA-sponsored conferences (S. 3892–3901). , American Statistical Association: Alexandria, VA. https://doi.org/10.2139/ssrn.2879551
- Kern, C. (2015). Modeling mobility dispositions from a multilevel perspective. Schmollers Jahrbuch, 135(1), 23–34. https://doi.org/10.3790/schm.135.1.23
- Kern, C. & Stein, P. (2015). Comparing coefficients of nonlinear multivariate regression models between equations. Survey Research Methods : SRM, 9(3), 159–167. https://doi.org/10.18148/srm/2015.v9i3.6211
- Stein, P. & Kern, C. (2015). Dyadische Modellierung regionaler Arbeitsmarktmobilität. In S. Lessenich (eds.), Routinen der Krise – Krise der Routinen : Verhandlungen des 37. Kongresses der Deutschen Gesellschaft für Soziologie in Trier 2014 (S. 520–531). , DFG: Trier.
- Kern, C. (2014). Regional Structures and Mobility Dispositions: A Multilevel Proportional- & Partial-Proportional Odds Approach. SOEP papers on Multidisciplinary Panel Data Research, 681. Berlin: DIW. https://doi.org/10.2139/ssrn.2493094
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Projects