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PRODID:-//DSO - EURO Working Group on Data Science meets Optimization - ECPv6.15.13.1//NONSGML v1.0//EN
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X-ORIGINAL-URL:https://www.euro-online.org/websites/dso
X-WR-CALDESC:Events for DSO - EURO Working Group on Data Science meets Optimization
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20260816
DTEND;VALUE=DATE:20260817
DTSTAMP:20260801T155652
CREATED:20260714T221253Z
LAST-MODIFIED:20260714T221253Z
UID:703-1786838400-1786924799@www.euro-online.org
SUMMARY:The Eighth Data Science Meets Optimization (DSO) Workshop at IJCAI 2026 in Bremen\, Germany
DESCRIPTION:Data science and optimization are tightly intertwined. Many problems in data science can be solved using optimizers. On the other side\, most optimization problems stated through classical models such as those from mathematical programming cannot be considered independent of historical data. Examples are ample: Methods aimed at high-level combinatorial optimization have been shown to strongly benefit from configuration\, algorithm selection and tuning tools that are learned from historical data; Machine Learning (ML) often relies on optimization techniques such as linear or integer programming\, and increasingly so for verification and optimal decision trees; Metaheuristic approaches characterized by learning components are commonplace in mathematical optimization; Black-box optimization makes heavy use of machine learning; deep learning is increasingly adopted to predict solutions to combinatorial problems (such as routing and scheduling problems). Furthermore\, ML models are embedded into the combinatorial optimization pipeline to address hard-to-model systems\, to validate the ML model itself\, or intertwined in methodologies like decision-focused learning. Not least\, LLMs are used to help formulate and solve optimization problems\, and optimization techniques can help verify LLM outputs\, for example.
URL:https://www.euro-online.org/websites/dso/event/the-eighth-data-science-meets-optimization-dso-workshop-at-ijcai-2026-in-bremen-germany/
LOCATION:Congress Centrum Bremen\, Findorffstraße 10\, Bremen\, Germany
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20270823
DTEND;VALUE=DATE:20270828
DTSTAMP:20260801T155652
CREATED:20260714T223454Z
LAST-MODIFIED:20260714T223454Z
UID:709-1818979200-1819411199@www.euro-online.org
SUMMARY:3rd EURO PhD School on Data Science meets Combinatorial Optimization
DESCRIPTION:Machine learning (ML) and data science (DS) methods have been at the forefront of multiple recent developments in Operations Research (OR). On the one hand\, ML/DS methods have been harnessed for handling uncertainty and automatically creating exac and heuristic solving procedures. On the other\, (discrete) optimization approaches have been used to enhance ML\, especially deep neural network\, approaches. Advancing these two fields poses challenges due to the breadth of knowledge required in both the fields of OR and ML/DS. The aim of this PhD school is to provide PhD students with the latest work on these two rapidly advancing fields through hands-on tutorials from leading experts\, thus positioning them on the forefront of these rapidly advancing fields. \nThe following subjects will be covered: \nAutomated heuristic discovery\, Decision-focused learning\, ML: Trustworthy AI and Explainability for OR\, Verifying and Reducing Neural Networks \nLecturers and further details are to be announced.
URL:https://www.euro-online.org/websites/dso/event/3rd-euro-phd-school-on-data-science-meets-combinatorial-optimization/
LOCATION:University of Vienna\, Austria
ORGANIZER;CN="Kevin Tierney%2C Patrick De Causmaecker (coordinator EWG/DSO)%2C Yingqian Zhang (coordinator EWG/DSO)":MAILTO:kevin.tierney@univie.ac.at
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