Background
Optimization problems appear everywhere in society. Think for example of vehicle routing, chip manufacturing, energy networks, pricing and drug design. Similar problems also appear in other scientific disciplines, for example biology and physics. The field of Operations Research aims to develop the mathematics and algorithmics needed to solve such optimization problems efficiently in practice. Traditionally, methods were developed purely based on mathematical models and analyzed with either worst-case scenarios or specific practical data sets. Recent research focuses on using large amounts of data in the development of optimization algorithms to develop methods that work well (in terms of accuracy as well as efficiency) for all or almost all data sets that may arise in practice. To achieve this, it is necessary to use machine learning within traditional optimization methods. Doing so, new challenges arise such as fairness, explainability, robustness and privacy.
About the workshop
This four-day workshop will bring together leading researchers in the field to discuss recent advancements, explore key challenges, and foster new collaborations.
The workshop aims to strengthen and widen research on all types of optimization, combined with any form of machine learning, both on a fundamental level as well as on direct applications, with an emphasis on the responsible use of machine learning. The main focus is on the use of machine learning within the algorithm design.
The programme will feature keynote lectures, contributed and lightning talks, and provide ample time for research discussions.
Tentative programme
We are delighted that the following speakers have accepted our invitation to give keynote lectures at the workshop:
- Francis Bach (INRIA, France)
- Marleen Balvert (Tilburg University, NL)
- Dimitris Bertsimas (MIT, USA)
- Andrea Lodi (Cornell Tech, USA)
- Bart van Parys (CWI, NL)
- Phebe Vayanos (University of Southern California, USA)
- more to be announced
