EURO Bulletin Board - Webinars
Next EURO ORML Seminar - October 12, 2026, 16:30 CEST. Prof Maria Grazia Speranza: Optimization and machine learning for decision making: competitors or partners?
https://euroorml.euro-online.org/
The EURO OSS on Operational Research and Machine Learning is an online seminar series with the goal to brand the role of Operational Research in Artificial Intelligence. The format is a weekly session that takes place every Monday, 16.30-17.30 (CET). It is 100% online-access, and it has leading speakers from Operational Research, as well as neighbouring areas, that will cover important topics such as explainability, fairness, fraud, privacy, etc. We also have the YOUNG EURO OSS on Operational Research and Machine Learning. In each YOUNG session, three junior academics will show their latest results in this burgeoning area. All talks are recorded, and the videos are uploaded to the website.
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You will receive information about our upcoming seminars and the link to participate. Our seminars are on Mondays, 16.30-17.30 CET, and we typically send the link on Monday morning.
October 12, 2026, 16.30 – 17.45 (CET)
Optimization and machine learning for decision making: competitors or partners?
Speaker: Prof Maria Grazia Speranza
Full professor of Operations Research
Department of Economics and Management
University of Brescia
Abstract:
Machine learning (ML) is a constantly growing research area. While optimization researchers are trying to understand how to take advantage of ML methods, ML researchers are developing algorithms to solve classical optimization problems. Given the huge impact of ML in a large variety of disciplines and application domains, an underlying discussion topic concerns the possibility that ML may become capable of replacing optimization. In this talk I will discuss the different roles of optimization and supervised learning in decision making processes and provide a high level introduction to the benefits that one can have on the other. I will also present a comparison of reinforcement learning and classical optimization algorithms applied to a traveling salesman problem where uncertainty plays a major role.
Posted on 2026-09-12 by Sarah Fores