Teaching and Learning Concurrent Programming in the Shared Memory Model

Modern computers contain an increasing number of cores. As such, students of computer science need to be familiar with concurrent programming in order to be able to write correct and performant programs for these systems. In his PhD thesis Filip Strömbäck investigates what areas of concurrent programming students struggle with, and suggests ways in which teachers can help students to better understand the subject.

Bayesian Models for Spatiotemporal Data from Transportation Networks

In this thesis, we develop Bayesian models with applications to transportation. We specifically focus on models that can be trained on spatiotemporal data coming from transport networks to make predictions on, e.g., bus delays or the actual network topology. Special attention has been given to model scalability issues and uncertainty quantification. We have used real-world data from transportation systems in every study to keep a balance between statistical rigor, novelty, and applicability.

Network-based Anomaly Detection for SCADA systems

Supervisory Control and Data Acquisition (SCADA) systems monitor critical infrastructures such as power grid. As SCADA systems start adapting to the Internet, cyber attacks against them become an attractive goal for attackers. In her PhD thesis Chih-Yuan Lin presents new approaches for intrusion detection that exploits the inherent timing characteristics present in a running system and alert when anomalies arise.