Mark Verhagen

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Sociology (2019 cohort)

I am interested in pragmatically applying computational methods into the standard workflow of the empirical social scientist. My work focuses on many of the well-documented problems of classical approaches to quantitative work and how these can be addressed by selectively applying methods from the domains of Artificial Intelligence and Machine Learning. Examples of such problems are unreasonable linearity and additivity assumptions, the complete lack of addressing researcher degrees of freedom and the difficulties in generating holistic insights across different findings. I argue that computational methods can be used throughout the classical approaches and that they are complementary to one another. In other words: I don't call for the wholesale substitution of classical methods for their computational alternatives, but rather for a symbiosis of the two.

My research illustrates the added value of computational methods through application in substantive case studies within the social sciences, as well as simulation results. My substantive cases focus on educational research and the neighbourhood effect.

During my undergrad I combined a B.A. in Art History with a B.Sc. in Econometrics at the University of Amsterdam. I did M.Sc’s in both Econometrics and Sociology, at the University of Amsterdam and University of Oxford respectively. During my studies I’ve founded the Analytics Academy, a non-profit institution for Econometrics students to help NGO’s and cultural institutions in the Amsterdam areas through their quantitative know-how. I’ve also founded two data science consulting firms, Delph and Apadana, through which I’ve helped companies put their data to good use.