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.