The Spatiotemporal Data Analysis (SDA) research group does research resulting in methodological advances in machine learning, including uncertainty-aware learning, flow matching, representation learning, and multi-objective (hierarchical) reinforcement learning. These methods are designed to address the challenges of data efficiency, generalization, and interpretability, especially in high-stakes domains such as GNSS resilience, vision and sensor systems, and sustainable urban planning.
Yle interviewed Professor Laura Ruotsalainen, the leader of SDA group, about GNSS signal jamming on 9th Jan 2024 (in Finnish). Read the article to learn more about our GNSS research and how jamming works!