Prof. Dr. Nihat Ay

  • Hamburg University of Technology,
    Professor and Head of the Institute for Data Science Foundations
  • Leipzig University, Honorary Professor
  • Santa Fe Institute, Professor
  • Arizona State University, Adjunct Professor

phone: +49 40 42878 4931
mail: nihat.ay(at)tuhh.de

Nihat Ay
Photograph by Douglas Merriam for the Santa Fe Institute


Research interests:

  • Complexity and information theory
  • Mathematical theory of learning in neural networks and cognitive systems
  • Graphical models (Bayesian networks) and their application to causality theory
  • Information geometry and its applications to complexity and network robustness
  • Geometric structure in quantum theory (non-commutative state spaces)

Short Curriculum Vitae
Nihat Ay studied mathematics and physics at the Ruhr University Bochum and received his Ph.D. in mathematics from the Leipzig University in 2001. In 2003 and 2004, he was a postdoctoral fellow at the Santa Fe Institute and at the Redwood Neuroscience Institute (now the Redwood Center for Theoretical Neuroscience at UC Berkeley). After his postdoctoral stay in the USA, he became an assistant professor (wissenschaftlicher Assistent) at the Mathematical Institute of the Friedrich Alexander University in Erlangen. From September 2005 to March 2021, he worked as a Max Planck Research Group Leader at the Max Planck Institute for Mathematics in the Sciences in Leipzig where he was heading the group Information Theory of Cognitive Systems. As a part-time professor of the Santa Fe Institute he is involved in research on complexity and robustness theory. He is also affiliated with the Leipzig University as an honorary professor for information geometry. Nihat Ay has co-authored a comprehensive mathematics book and written numerous articles on this subject. Furthermore, he serves as the Editor-in-Chief of the Springer journal Information Geometry. In April 2021, he joined the Hamburg University of Technology as a professor and the head of the newly founded institute for Data Science Foundations.


Publications