Hybrid seminar announcement – Dr. Martina Fischetti
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Alice Raffaele
May 18, 2025, 6:30:22 AMMay 18
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Dear AIROYoungers,
I'm sorry for cross-posting.
I am forwarding the announcement of another hybrid seminar next Wednesday, May 21st, at the Department of Information Engineering of the University of Padova. The speaker will be Dr. Martina Fischetti, tenure-track researcher at the University of Seville, Spain (and one of the founders of AIROYoung, if you didn't know ;) ).
You can find all the details below.
Have a nice Sunday!
Best,
Alice
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Speaker: Dr. Martina Fischetti
When: May, 21st @11:30
Where: Aula Riunioni DEI/D
Title: Machine Learning plus Operations Research for Optimal Counterfactual Controller in Wind Turbines
When: May, 21st @11:30
Where: Aula Riunioni DEI/D
Title: Machine Learning plus Operations Research for Optimal Counterfactual Controller in Wind Turbines
Zoom link: https://unipd.zoom.us/j/85023866243
Abstract:
Machine Learning (ML) models are increasingly used in businesses to detect faults and anomalies in complex systems. In this work, we take this approach a step further. Once an anomaly is detected, we aim to identify the optimal control strategy that not only restores the system to a safe state but also minimizes the disruption or changes required to do so. We frame this challenge as a counterfactual problem: given an ML model that classifies system states as either good or faulty; our goal is to determine the minimal adjustment to the system’s features (i.e., its current status) necessary to return it to the good state.
To achieve this, we leverage a mathematical model that finds the optimal counterfactual solution while respecting system-specific constraints. Notably, most counterfactual analysis in the literature focuses on individual cases where a person seeks to alter their status relative to a decision made by a classifier—such as for loan approval or medical diagnosis. In contrast, our work tackles an entirely different problem: optimizing counterfactuals for a complex energy system, specifically in the context of offshore wind turbine oil transformers.
We applied this novel methodology to the maintenance of offshore wind turbine oil transformers, demonstrating its impact through a real-world application in collaboration with our industrial partner, Vattenfall. This application not only advances counterfactual optimization in a new domain but also opens avenues for broader research in this area.
Our tests on real-world data provided by our industrial partner show that our methodology easily adapts to user preferences and brings savings in the order of 3 million euro per year in a typical farm.
Short Bio:
Martina Fischetti is a tenure-track researcher at the University of Seville, Spain. She holds M.Sc. degrees from the University of Padova (March 2014) and the University of Aalborg (June 2014) in Automation Engineering. In March 2018, she finished her Industrial PhD in OR at the Technical University of Denmark in collaboration with Vattenfall (the lead energy company in North Europe). Her PhD thesis was titled Mathematical Programming Models and Algorithms for
Offshore Wind Park Design. Her PhD work on the optimization of wind farm design and cable routing has been awarded various international prizes, such as the Best Industrial PhD from Innovation Fund Denmark (2019), EURO Doctoral Dissertation Award (2019), Glover-Klingman Prize (2018), AIRO Best Application Paper award (2018), the Best Student Paper Award ICORES (2017), and finalist positions at the EURO Excellence in Practice award (2018) and the prestigious INFORMS Franz Edelman award (2019). She was also selected as a role model for young women in OR by the EURO WISDOM forum in 2021. After her PhD, she worked in industry (lead engineer in Vattenfall BA Wind, specializing in OR) and in government institutions (at the Joint Research Center of the European Commission in Seville, Spain, where she applies
Operations Research to European transport challenges).
Abstract:
Machine Learning (ML) models are increasingly used in businesses to detect faults and anomalies in complex systems. In this work, we take this approach a step further. Once an anomaly is detected, we aim to identify the optimal control strategy that not only restores the system to a safe state but also minimizes the disruption or changes required to do so. We frame this challenge as a counterfactual problem: given an ML model that classifies system states as either good or faulty; our goal is to determine the minimal adjustment to the system’s features (i.e., its current status) necessary to return it to the good state.
To achieve this, we leverage a mathematical model that finds the optimal counterfactual solution while respecting system-specific constraints. Notably, most counterfactual analysis in the literature focuses on individual cases where a person seeks to alter their status relative to a decision made by a classifier—such as for loan approval or medical diagnosis. In contrast, our work tackles an entirely different problem: optimizing counterfactuals for a complex energy system, specifically in the context of offshore wind turbine oil transformers.
We applied this novel methodology to the maintenance of offshore wind turbine oil transformers, demonstrating its impact through a real-world application in collaboration with our industrial partner, Vattenfall. This application not only advances counterfactual optimization in a new domain but also opens avenues for broader research in this area.
Our tests on real-world data provided by our industrial partner show that our methodology easily adapts to user preferences and brings savings in the order of 3 million euro per year in a typical farm.
Short Bio:
Martina Fischetti is a tenure-track researcher at the University of Seville, Spain. She holds M.Sc. degrees from the University of Padova (March 2014) and the University of Aalborg (June 2014) in Automation Engineering. In March 2018, she finished her Industrial PhD in OR at the Technical University of Denmark in collaboration with Vattenfall (the lead energy company in North Europe). Her PhD thesis was titled Mathematical Programming Models and Algorithms for
Offshore Wind Park Design. Her PhD work on the optimization of wind farm design and cable routing has been awarded various international prizes, such as the Best Industrial PhD from Innovation Fund Denmark (2019), EURO Doctoral Dissertation Award (2019), Glover-Klingman Prize (2018), AIRO Best Application Paper award (2018), the Best Student Paper Award ICORES (2017), and finalist positions at the EURO Excellence in Practice award (2018) and the prestigious INFORMS Franz Edelman award (2019). She was also selected as a role model for young women in OR by the EURO WISDOM forum in 2021. After her PhD, she worked in industry (lead engineer in Vattenfall BA Wind, specializing in OR) and in government institutions (at the Joint Research Center of the European Commission in Seville, Spain, where she applies
Operations Research to European transport challenges).
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