Postdoctoral Researcher
Mark focuses in his work on including computational methods into the workflow of the empirical social scientist. He studies some of the well-documented problems of classical approaches to quantitative work and how these can be tackled by selectively applying methods from other disciplines using more flexible methods, like those in machine learning. Examples of such problems are linearity and additivity assumptions, and issues around researcher degrees of freedom. Mark argues that computational methods can be used hand-in-hand with classical approaches to empirical work, and that they are complementary to one another. To illustrate the added value of computational methods, Mark applies these approaches in both substantive case studies in the social sciences, as well as simulation results. Some of these cases include topics from Educational Research, Law and Economics, and Health, amongst others.
Before joining the Leverhulme Centre in 2020, Mark started his DPhil in Sociology at Nuffield College in 2019. Before that, he obtained MSc degrees in Sociology (Oxford) and Econometrics (University of Amsterdam), and also holds bachelor degrees in Art History and Econometrics (University of Amsterdam). His work has been published in varied journals in different fields, like BMC Medicine, Plos One, PNAS and The International Review of Law and Economics.
Mark and co-authors' work on Learning Loss due to school closures during the Covid-19 pandemic was featured extensively in the news, for example in The Financial Times, The Economist, The New York Times, and many other national and international outlets.
Currently, Mark is in the process of finishing up his DPhil. Besides his academic interests, Mark is an active participant in the Data Science ecosystem in the Amsterdam area having worked for various research institutes, government bodies, and other commercial or non-profit organisations. He enjoys the challenge of putting data to good use.
Mark Verhagen
PhD
Postdoctoral Researcher
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Mark focuses in his work on including computational methods into the workflow of the empirical social scientist. He studies some of the well-documented problems of classical approaches to quantitative work and how these can be tackled by selectively applying methods from other disciplines using more flexible methods, like those in machine learning. Examples of such problems are linearity and additivity assumptions, and issues around researcher degrees of freedom. Mark argues that computational methods can be used hand-in-hand with classical approaches to empirical work, and that they are complementary to one another. To illustrate the added value of computational methods, Mark applies these approaches in both substantive case studies in the social sciences, as well as simulation results. Some of these cases include topics from Educational Research, Law and Economics, and Health, amongst others.
Before joining the Leverhulme Centre in 2020, Mark started his DPhil in Sociology at Nuffield College in 2019. Before that, he obtained MSc degrees in Sociology (Oxford) and Econometrics (University of Amsterdam), and also holds bachelor degrees in Art History and Econometrics (University of Amsterdam). His work has been published in varied journals in different fields, like BMC Medicine, Plos One, PNAS and The International Review of Law and Economics.
Mark and co-authors' work on Learning Loss due to school closures during the Covid-19 pandemic was featured extensively in the news, for example in The Financial Times, The Economist, The New York Times, and many other national and international outlets.
Currently, Mark is in the process of finishing up his DPhil. Besides his academic interests, Mark is an active participant in the Data Science ecosystem in the Amsterdam area having worked for various research institutes, government bodies, and other commercial or non-profit organisations. He enjoys the challenge of putting data to good use.