Projects and Grants

Team members focus on research, development, and implementation of computational intelligence methods for the analysis of uncertain information, such as biosensor and social science data with systematic errors. In the analytical part, they focus on hypothesis generation, non-statistical processing of time series with detection and prediction of structural changes, and machine learning, including deep neural networks. Research on knowledge systems aims at expert knowledge bases using Explainable AI (XAI) principles in combination with data-driven and compositional models. This is crucial for personalized digital interventions and mHealth, where data-driven approaches are insufficient to determine the appropriate intervention, which requires expert knowledge and logical methods of interpretation.

Within the project carried out in collaboration with the company NAM System, software is being developed for optimizing routes for waste collection vehicles. The goal is to improve efficiency of waste collection processes and reduce operational costs.