Description
One of our focus areas is mining rich and complex data sources, with emphasis on sequential and temporal data, histogram data, text, and graphs. In particular, we are interested in (1) learning predictive models, such as ensemble models, for classification of complex temporal data sources, such as univariate and multivariate time series, event sequences, sequences of temporal intervals, and graphs, (2) time series prediction and event burst detection using statistical methods and deep learning, and (3) subgroup and rule discovery in transactional and sequential data.
Latest publications
Implementations
- wildboar - explainable machine learning library for time series in Python
Related Projects
People
Panagiotis Papapetrou, Professor
sequential and temporal data mining, explainability, healthcare applications
Isak Samsten, Senior Lecturer
explainability, temporal data mining, fintech
Jaakko Hollmén, Senior Lecturer
probabilistic modeling, environmental sustainability, healthcare applications
Ioanna Miliou, Senior Lecturer
nowcasting and forecasting, data science for social good with applications in healthcare, epidemics and peace
Zed Lee
mining temporal abstractions from complex data sources
Zhendong Wang
explainable sequential models
Franco Rugolon
explainable machine learning for healthcare
Tim Kreuzer
time series analysis for digital twins