I’m a philosopher of science in the School of Politics, Philosophy, and Area Studies at the University of East Anglia. My research focuses on issues related to modelling and measurement in the social sciences, the epistemology of artificial intelligence, and mechanistic explanation in biology and cognitive science.
My previous positions were at Technische Universität Berlin, where I was a Postdoc at the Institut für Philosophie, Literatur, Wissenschafts und Techniksgeschichte, at the University of Copenhagen, where I was part of the project: Living Machines? Philosophical perspectives on the engineering approach in biology, at the Center for Philosophy of Natural and Social Sciences, at the London School of Economics, and at the Center for Philosophy of Science at the University of Pittsburgh. I did my PhD on the structure of explanations in cognitive neuroscience at the University of East Anglia.
Values and standards in psychological measurement While we commonly view psychological measurement as a crucial tool for gaining reliable insights into human capacities and behaviour, its widespread application sparks ongoing discussions on reliability. When and why should we place confidence in the results of psychological assessments and tests? In this project, I assert that our trust is justified within a framework of scientific practices. These practices encompass technical scrutiny, interdisciplinary validations, and social interventions, collectively shaping the values and interests inherent in psychological measurement methods.
Human and machine creativity in scientific discovery Scientific discovery, often perceived as a romantic and creative endeavour, has been deemed unanalyzable, and contrasted with the logical nature of the subsequent validation and verification processes. A renewed philosophical interest in scientific discovery has paved a new way of understanding the ampliative rules, methods, and heuristic procedures that make up the human practices of scientific discovery. This project critically examines the ways in which machine learning methods transform and creatively enhance discovery in various scientific domains.
This site contains information about my papers, teaching, work in progress, the conferences and workshops in which I am involved. For more information, you can consult my resume or contact me at email@example.com