I am a philosopher of science and technology affiliated with the Munich Center for Technology in Society at Technical University Munich. Before turning to philosophy, I studied physics at Humboldt Universität zu Berlin and science writing at Freie Universität Berlin. My PhD thesis was on fundamental concepts in statistical mechanics centering around the problem of the arrow of time in physics. I have been a visiting researcher in various places including the Centre for Philosophy of Natural and Social Science at the London School of Economics, the Center for Philosophy of Science of the University of Pittsburgh and the Institute for Public Knowledge of New York University. I was a Poiesis Fellow of the BMW foundation and have been for many years the vice chairman of the working group on philosophy of physics of the German Physical Society.
My main research interest concerns scientific method. I explore, how methodological approaches differ between various disciplines. In recent years, I have particularly looked into the epistemology of the engineering sciences and into the epistemology of data science. These case studies convinced me of the necessity of reviving and defending an inductivist approach to scientific method against the prevailing hypothetico-deductivism. To this purpose, I work on various interrelated concepts like causation or probability as well as on different types of inductive inferences, in particular on analogical reasoning. I try to develop these concepts from what Federica Russo has aptly termed a ‘variational rationale’ in contrast to enumerative approaches. More specifically, my approach to causation is based on difference making and the account of probability puts the role of symmetries rather than relative frequencies at the center of the analysis. I have published a brief introductory book on the philosophy of science of Big Data. I also currently work on a more comprehensive volume that spells out in detail the underlying epistemological argument and by defending inductivism aims to provide an epistemological foundation for data science.