Professor Miklós Rédei

Miklós Rédei is professor of philosophy in the Department of Philosophy, Logic and Scientific Method at the London School of Economics and Political Science (LSE). Before taking up the position at LSE in 2007, he had been affiliated with the Department of Logic and with the Department of History and Philosophy of Science at Lorand Eotvos University, Budapest, Hungary.

Professor Redei was a Visiting Fellow and Fulbright Scholar at the Center for Philosophy of Science in Pittsburgh, U.S.A.; he was a Senior Resident Fellow at the Dibner Institute for History of Science and Technology at MIT; and he had visiting positions in the Foundations of Physics Group in Utrecht, the Netherlands, and in the Department of Logic and Philosophy of Science at the University of California at Irvine, U.S.A.

His research area is philosophy and foundations of modern physics, especially quantum theory, and he worked on probabilistic causality and on foundations of classical and quantum probability. Professor Redei also did extensive research on John von Neumann's life and work. He is the author of "Quantum logic in Algebraic Approach" (Kluwer, 1998); with two co-authors published the book "The Principle of the Common Cause" (Cambridge University Press, 2013) and edited several volumes, including "John von Neumann: Selected Letters" (American Mathematical Society and London Mathematical Society, 2015).

He was chair, co-chair and steering committee member of three major European Science Foundation grants that provided opportunities to develop philosophy of science in Europe in the period 2003-2013. He is a founding members of the European Philosophy of Science Association (EPSA) and was elected to serve on the EPSA Steering Committee of EPSA. Professor Redei received the Carl-Friedrich Siemens Research Award from the Alexander von Humboldt Foundation in 2018, which he used to stay in MCMP during the 2018-2019 academic year.

Research Interests

• Philosophy of science
• Philosophy and foundations of quantum physics
• Foundations of probability theory
• Bayesian learning