Didier Sornette

Professor on the Chair of Entrepreneurial Risks at ETH Zurich

SFI Faculty Member at Swiss Finance Institute

Biography

ETH Zurich

Didier Sornette is Professor on the Chair of Entrepreneurial Risks at the Swiss Federal Institute of Technology Zurich (ETH Zurich) since March 2006. He is also a professor of the Swiss Finance Institute, and a professor associated with both the department of Physics and the department of Earth Sciences at ETH Zurich. In 2008, he launched the Financial Crisis Observatory to test the hypothesis that financial bubbles can be diagnosed in real-time and their termination can be predicted probabilistically. He is a founding member of the Risk Center at ETH Zurich since June 2011. He is a PI at the Future Resilient Center at the National University of Singapore and is a specially appointed professor of Tokyo Institute of Technology since Nov. 2016 in the new Institute of Innovative Research. Prof. Didier Sornette uses rigorous data-driven mathematical statistical analysis combined with nonlinear multi-variable dynamical models including positive and negative feedbacks to study the predictability and control of crises and extreme events in complex systems, with applications to financial bubbles and crashes, earthquake physics and geophysics, the dynamics of success on social networks and the complex system approach to medicine (immune system, epilepsy and so on) towards the diagnostic of systemic instabilities.

Swiss Finance Institute

Didier Sornette is Emeritus Professor of Entrepreneurial Risks at ETH Zurich, and Chair Professor and co-Dean of the Institute of Risk Analysis, Prediction, and Management (Risks-X) at the Southern University of Science and Technology (SUSTech) Shenzhen. Since his recent retirement from ETH Zurich, Professor Sornette has been working actively with the private sector on developing socially important products with clear applications in the medical field and in dynamic financial risk management.

Expertise

Professor Sornette is using data-driven mathematical statistical analysis to study the predictability and control of crises and extreme events in complex systems. His key contribution is to use nonlinear multi-variable dynamical settings that include both positive and negative feedback. The results obtained help us better understand the overall stability and instability of financial markets.

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