Timo Koch is a postdoctoral researcher at the Institute of Behavioral Science and Technology at the University of St. Gallen. He is passionate about leveraging technology (e.g., artificial intelligence, natural language processing, mobile sensing) to enhance peoples’ and society’s health and well-being. His research interests lie in the analysis of behavioral and language data, (interpretable) machine learning, and Human-AI interaction.
Timo works in the SNSF-funded ACTWELL (”Activities, Contexts, and Traits in Well-Being in Everyday Life Longitudinally”) project that investigates digital traces of well-being in smartphone data.
He received his bachelor’s degree in Psychology with a minor in Business Administration at University of Mannheim. He obtained his master’s degree in Work, Organizational and Social Psychology at LMU Munich, where he also completed his Ph.D. in Psychological Methods. During his studies, Timo visited the Psychometrics Centre at the University of Cambridge and the Media & Personality Lab at Stanford University.
Koch, T. K., Romero, P., & Stachl, C. (2022). Age and gender in language, emoji, and emoticon usage in instant messages. Computers in Human Behavior, 126, 106990. https://doi.org/10.1016/j.chb.2021.106990
Koch, T. K., Frischlich, L., & Lermer, E. (2023). Effects of fact-checking warning labels and social endorsement cues on climate change fake news credibility and engagement on social media. Journal of Applied Social Psychology, 1-13. https://doi.org/10.1111/jasp.12959
Koch, T. K., Pargent, F., Kleine, A., Lermer, E., & Gaube, S. (2023). A Tutorial on Tailored Simulation-Based Power Analysis for Experimental Designs with Generalized Linear Mixed Models. PsyArXiv. https://doi.org/10.31234/osf.io/rpjem
Hummelsberger P., Koch, T. K., Rauh, S., Dorn, J., Lermer, E., Raue, M., Hudecek, M., Schicho, A., Colak, E., Ghassemi, M., Gaube, S. (2023). Insights on the Current State and Future Outlook of Artificial Intelligence in Healthcare From Expert Interviews. JMIR AI, https://doi.org/10.2196/47353
Gaube, S., Suresh, H., Raue, M., Lermer, E., Koch, T. K., Hudecek, M. F. C., Ackery, A. D., Grover, S. C., Coughlin, J. F., Frey, D., Kitamura, F. C., Ghassemi, M., & Colak, E. (2023). Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays. Scientific Reports, 13, 1383. https://doi.org/10.1038/s41598-023-28633-w