Data-Driven Control Systems Design

Data-driven control is an approach to control systems and automation that relies heavily on data analysis and machine learning techniques to make decisions and optimize system performance. Unlike traditional control methods that are based on mathematical models, data-driven control leverages large datasets and computational algorithms to develop control strategies and make real-time adjustments.

Feedback Control for Finance

In finance applications, control theory provides a systematic and quantitative approach to decision-making, enabling financial professionals and institutions to optimize performance, manage risk, and adapt to changing market dynamics. These applications have become increasingly important in the context of modern financial markets, where automation and data-driven decision-making are prevalent.

Automotive Controls

Automotive controls refer to the various systems and technologies used to manage and regulate the operation of vehicles, ensuring their safe and efficient performance. These controls encompass a wide range of functions and components, all aimed at enhancing the driving experience, improving safety, and optimizing vehicle efficiency.

Federico Porcari

1st year PhD Student

Federico Porcari received his bachelor’s degree cum laude in Automation and Control Engineering at Politecnico di Milano in September 2021. Continuing this M.Sc. studies at Politecnico di Milano, he spent a semester at the Delft University of Technology in the Netherlands as an Erasmus+ exchange student. Currently, he is concluding his thesis “Data-Driven Design of Anti-Windup Control Systems”, in which he developed new data-based algorithms to synthesize controllers accounting for saturation effects. He is going to graduate in December 2023 and join at the end of the same month the mOve research group as a Ph.D. candidate in Information Technology, with area Systems and Control.

Davide Raffaeli

1st year PhD Student

Davide Raffaelli graduated in Bioinformatics for Computational Genomics with a thesis on network approaches for the characterization of transcriptional regulations in fetal brain cells. Then, he spent more than a year in Banor Labs as a data scientist, developing systematic trading algorithms. Currently, he is working on his PhD under the supervision of professor Simone Formentin. His research focuses on machine learning based model predictive control for investment management.

Matteo Cercola

1st year PhD Student

Matteo Cercola graduated in Computer Science Engineering at Politecnico di Milano in December 2023 with the thesis “Neural network based LiDAR Segmentation for autonomous racing ”. Since December 2023, he has been working on his PhD at Learning to control (L2C) lab under the supervision of prof. Simone Formentin. His research focuses on developing AI-supported systems for the management of highway traffic events.

Andrea Sassella

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Andrea Sassella was born in Sondalo, Italy, on 18th April 1996. He received his B.Sc. and M.Sc. Degree cum laude in “Automation and Control Engineering”, respectively in July 2018 and in October 2020. During the M.Sc. he was also a student of Alta Scuola Politecnica and he spent five months at École Polytechnique Fédérale de Lausanne (EPFL) in Lausanne, Switzerland, as part of the Extra EU Exchange programme. His M.Sc. thesis concerned a new data-driven method for designing hybrid controllers for constrained systems, developed in the context of the Honours Programme. From Nov. 2020 to Oct. 2023, he was a PhD student in Systems and Control, at Dipartimento di Elettronica, Informazione e Bioingegneria of Politecnico di Milano.

Federico Dettù

Now Software Engineer at Sinergy Flow

Federico Dettù received his B.Sc. on Sep. 2017 and his M.Sc. cum laude on Oct. 2020 in Automation and Control Engineering from Politecnico di Milano. From January to September 2020, he was a Visiting Researcher at Stanford University, USA. From Nov. 2020 to Oct. 2023, he was a PhD student in Systems and Control, at Dipartimento di Elettronica, Informazione e Bioingegneria of Politecnico di Milano. Starting from Nov. 2023, he is an Embedded Software Engineer at Sinergy Flow, a deep-tech startup. His research interests concern data-based modelling, estimation and control approaches for control systems.

Valentina Breschi

Now Assistant Professor at TU Eindhoven

Valentina Breschi received her B.Sc. in Electronic and Telecommunication Engineering and her M.Sc. in Electrical and Automation Engineering from the University of Florence (Italy) in 2011 and 2014, respectively. She received her Ph.D. in Control Systems from IMT School for Advanced Studies Lucca (Italy) in 2018. From January to July 2017, she was a visiting scholar at the Department of Aerospace Engineering, University of Michigan (United States). From 2018 to 2020, she was a post-doctoral researcher at Politecnico di Milano (Italy), where she then held a fixed-term position as a junior assistant professor from 2020 to 2023. In 2023, she joined the Control Systems group at the Department of Electrical Engineering at Eindhoven University of Technology (The Netherlands) as an Assistant Professor. Her main research interests are data-driven control, system identification, collaborative learning, and human-centered optimal policy design with a focus on mobility systems.