Rate My Professor Caspar Van Lissa

Caspar van Lissa is an Associate Professor of Social Data Science in the Department of Methodology and Statistics at Tilburg School of Social and Behavioral Sciences, Tilburg University. He holds a Bachelor’s degree in Liberal Arts and Sciences with minors in statistics and neuroscience from University College Utrecht (2007), an MSc in Social Psychology cum laude from VU University Amsterdam (2011), and a PhD in Adolescent Development from Utrecht University (2016). Following his PhD, he completed a postdoctoral position in Family Sociology at Erasmus University Rotterdam (2015-2018). His career spans multiple Dutch universities, where he has taught diverse audiences from high school students to professionals and advanced his commitment to open science and educational innovation. Van Lissa chairs the Open Science Community Tilburg (OSCT) and serves as a board member of the Tilburg Young Academy, fostering interdisciplinary collaboration and team science.

Van Lissa’s research focuses on machine learning-informed theory construction, machine learning-informed research synthesis via meta-analysis and text mining systematic reviews, open science practices emphasizing computational reproducibility and workflow automation, adolescents’ socio-emotional development, parent-child conflict resolution, and fathers’ role in child development. He leads the INSIGHT lab, applying these approaches to fields like climate research, mental health, and well-being. Notable awards include the prestigious NWO Vidi grant (2024) for “From patterns to principles: using machine learning to construct social scientific theories,” an eScience Center spearhead grant, and the 2021 Teacher of the Year award from USocia for his Statistics 1 course at Liberal Arts and Sciences, which employs flipped classrooms, open science education, problem-based learning, and interactive eBooks. Key publications encompass “Toward a predictive model of moral concern” (Journal of Experimental Social Psychology, 2026), “Capturing causal claims: A fine-tuned text mining model for extracting causal sentences from social science papers” (Research Synthesis Methods, 2025), “Identifying prenatal risk factors of postpartum depression with machine learning” (Scientific Reports, 2025), “A novel, network-based approach to assessing romantic-relationship quality” (Perspectives on Psychological Science, 2024), and “A theory-informed emotion regulation variability index: Bray-Curtis dissimilarity” (Emotion, 2024). His contributions include developing free open-source research software and delivering workshops on open science.