We warmly welcome our new academic guest Cesare Caputo!

Their work will be focused on a novel Deep Reinforcement Learning approach to design and manage more adaptive and flexible infrastructure systems, by exploring a much larger action and uncertainty space than computationally tractable with analytical state of the art methods. The research topics will focus particularly on adaptive planning needed as a result of climate change impacts, as well as how to best improve the transparency and acceptability of these data-driven methods to the appropriate decision makers and stakeholders, perhaps through a decision-support system.

Cesare is currently a final year PhD student at Imperial College London, where his thesis topic is “A machine learning based framework for the design and evaluation of flexible, sustainable and resilient energy systems under uncertainty”, and his stay at ETHZ is funded by the Alan Turing Institute. Before starting his PhD, he obtained an MSc in Sustainable Energy Systems from University of Edinburgh and a BEng in Bioresource Engineering with a minor in Economics from McGill University.