YEQT XV "Machine Learning for Stochastic Networks"

The Young European Queueing Theorists (YEQT) workshop is organized annually and aims to bring together young researchers and world-leading experts. State-of-the-art research related to queueing theory, operations research, applied probability and related areas are discussed. The event provides an excellent opportunity for developing researchers to interact and exchange ideas in an informal, friendly, yet research-focused setting. The program typically consists of presentations by both senior and junior researchers, and several keynote presentations and tutorials by prominent researchers.

 

Theme and aim

This year’s theme for the YEQT workshop is Machine Learning for Stochastic Networks. The aim of YEQT XV is to bring together senior researchers who have made substantial contributions to advancing machine learning techniques and the optimization of stochastic networks. The methodological focus will be on research that combines theoretical stochastic modeling and optimization together with modern machine learning techniques.

 

To see the importance, consider for example that resource management in stochastic networks, such as occur in data centers and cloud networks, involves huge challenges due to increasingly heterogeneous applications with highly diverse traffic characteristics and performance requirements. Machine learning techniques and stochastic learning approaches offer tremendous potential to e.g. steer dynamic resource management and drive data replication in a self-optimizing manner.

 

YEQT XV is being organized by Jaron Sanders (TU/e), Peter Verleijsdonk (TU/e), and Kuang Xu (Stanford).

 

For more information and registration, see the workshop's website.