Safe Automation for Intelligent Systems


Peyman Mohajerin Esfahani

Delft University of Technology
Fri, Nov 19 2021 - 03:00 pm (GMT + 7)
About Speaker

Peyman Mohajerin Esfahani is an assistant professor in the Delft Center for Systems and Control, and a co-director of the Delft-AI Energy Lab at the Delft University of Technology. Prior to joining TU Delft, he held several research appointments at the Risk Analytics and Optimization Chair at EPFL, at the Automatic Control Laboratory at ETH Zurich, and at the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology between 2014 and 2016. He received the B.Sc. and M.Sc. degrees from Sharif University of Technology, Iran, and the PhD degree from ETH Zurich.

He was selected for the Spark Award by ETH Zurich for the twenty best inventions of the year in 2013, and received the SNSF Postdoc Mobility fellowship in 2015. He was one of the three finalists for the Young Researcher Prize in Continuous Optimization awarded by the Mathematical Optimization Society in 2016. He was a recipient of the 2016 George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society, an award that recognizes the best paper published in the past two years in the IEEE Transactions on Automatic Control. In 2020 he also received the ERC Starting Grant and the INFORMS Frederick W. Lanchester Prize for the best contribution to operations research and management science in the past five years.

His research interests include theoretical and practical aspects of decision-making problems in uncertain and dynamic environments, with applications to control and security of large-scale and distributed systems.


Digitalization offers ample opportunities for improving the monitoring, maintenance, and performance of future industrial systems. Harnessing this potential calls for novel mathematical foundations of automation along with scalable computational tools. This seminar focuses on the fault estimation problem in automation. We discuss concrete ideas that create synergy between traditional model-based approaches and modern data-driven analytics. The discussion will be motivated by an application of the lateral safety systems of automated vehicles.

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