Andrea Castelletti received an MSc degree in Environmental Engineering and a PhD in Information Engineering from Politecnico di Milano in 1999 and 2005. Since 2006 he is faculty member in the School of Civil and Environmental Engineering and the Department of Electronics, Information, and Bioengineering at the same university. He is also Adjunct Professor at the Centre for Water Research of the University of Western Australia and since 2014 Senior Scientist at ETH Zurich.
Dr. Castelletti is co-author of 2 international books on integrated water resources management and about 90 publications in international journals, book chapters and conference proceedings. Dr. Castelletti serves the scientific community as the chair of the IFAC Technical Committee TC8.3 on Modelling and Control of Environmental Systems and member of the ASCE/EWRI Environmental and Water Resources Systems Technical Committee (since 2011). He is Associate Editor of Water Resources Research, Environmental Modelling and Software, Journal of Water Resources Planning and Management, PlosONE, and Acta Geophysica. http://home.deib.polimi.it/castelle/home.html
My expertise and research interest are in the area of systems analysis and optimal control applied to water resources planning and management. My focus is in developing and applying new methodologies and tools to expand the scope of current management practice across sectors and to the river basin level by coping with the increased complexity of the coupled human-natural ecosystem (e.g. non-linearity, uncertainty, non stationarity, many feedbacks, multiple decision-makers, and multiple stakeholders) and accounting for current and projected societal, economic, and environmental needs. Within the SCCER-SoE Work Package 2 I will be involved in the development of an integrated model of the Swiss hydropower production system under changing climate and energy demand scenario (Task 2.5), combining high fidelity modelling of hydromorphological processes and advanced dynamic optimization approaches, including approximate dynamic programming, multi-objective evolutionary direct policy search.