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 Machine Learning meets Optimisation will be held during the school.  

This PhD school is organised by the Data Science meets Optimisation working group of EURO (European Association of Operational Research). See the first edition of the PhD school here. 

In cooperation with SIKS, SIKS PhD students can attend the school free of charge (limited places available).