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Second EURO PhD School Data Science Meets Combinatorial Optimisation

August 25, 2025 - August 29, 2025

The Second EURO PhD School Data Science Meets Combinatorial Optimisation took place at Eindhoven University of Technology, in september 2025

Lecturers

  • Prof. Carola Doerr Sorbonne University, France Black-Box Optimization
  • Prof. Kevin Tierney Bielefeld University, Germany Deep Reinforcement Learning for Vehicle Routing Problems
  • Prof. Kate Smith-Miles University of Melbourne, Australia Instance Space Analysis
  • Yingqian Zhang, Sicco Verver Eindhoven University of Technology, Delft University of Technology Optimization in Machine Learning
  • Kate Smith-Miles University of Melbourne Instance Space Analysis
  • Prof. Pieter Smet KU Leuven, Belgium Uncertainty in Optimization
  • Prof. Sicco Verwer Delft University of Technology, Netherlands Learning Optimal, Robust Decision Trees
  • Symposium Day with
    • Prof. Ilker Birbil, University of Amsterdam
    • Prof. Zaharah Bukhsh TU Eindhoven
    • Prof. Kate Smith-Miles University of Melbourne
    • Prof. Michael Römer Bielefeld University
    • Prof. Neil Yorke-Smith TU Delft
    • Prof. Yaoxin Wu TU Eindhoven
    • Dr. Pavel Troubil Dassault Systemes
    • Cynthia Luijkx ORTEC

Organizers

  • Yingqian Zhang Eindhoven University of Technology Scientific chair & organizer, Executive committee member EWG/DSO
  • Yi-Ming Yong, Igor Smit, Xia Jiang, Eindhoven University of Technology, Local organizers
  • Patrick de Causmaecker KU Leuven Scientific chair / coordinator EWG/DSO
Algorithms for combinatorial optimization feature aspects of data science in various respects. Combinatorial optimization problems (COPs) are mostly NP-hard, and this complexity reflects itself in complicated  and large solution spaces. Combinatorial optimization problems often originate from real-world problems and this real-world context has an impact on the set of instances likely to ask for a solution which influences  the applicability of specific algorithms. NP-hard problems often allow fast solutions for classes of instances while other classes are much harder to solve. Mapping these classes onto a space of instances provides insight and increases understanding in the problem as well as on the applicability of specific algorithms. In recent years, many advanced machine learning (ML) techniques have been developed to solve combinatorial optimization problems (COPs) directly or to aid algorithms in reaching good solutions more quickly. In addition, optimization techniques have been used to help build more transparent and fair machine learning models. This school will focus on topics and techniques that leverage data, machine learning, and optimization methods, taught by experts from OR and ML. 

On each day of the PhD school, one lecturer, often assisted by a post-doc, will teach a state-of-the art topic, providing both theory and hands-on training and exercises. In addition to those teaching sessions, PhD students will get the opportunity to present and discuss their work. Moreover, there will be several invited talks on new research of machine learning and optimization. Last but not least, during joint meals and social activities, there will be plenty of room for socializing and networking. In addition, a symposium on “AI meets Optimization” will be held during the school. 

Details

Venue

  • Eindhoven University of Technology
  • Eindhoven, Netherlands + Google Map