Leader: Maciej Grzenda
The vision of the Stream Mining and Urban Data Systems Group is to combine research on machine learning methods for data streams with building software platforms, systems, and applications, combining storage and analytical platforms for urban data processing. By applying novel stream mining methods, we aim to address the dynamics of urban systems. To address challenges faced by inter alia urban environments, we develop novel data preprocessing and machine learning methods. By applying Data Science methods to evolving urban systems, we aim to contribute to the social good.
Leader: Paweł Rzążewski
We study the interplay between the structure of graphs and the algorithms that can efficiently solve problems on them. We seek to identify structural properties — like planarity, treewidth, or sparsity — that make otherwise hard problems tractable. We aim to bridge combinatorial insights and computational methods, aiming to understand both what graphs look like and how we can compute on them effectively.