Research Interests

Mobility, Sequential, and Multidimensional Data Mining
Research in this area focuses on extracting meaningful patterns and insights from large-scale spatiotemporal datasets, such as human or animal mobility traces, sensor data, and user interactions. It involves techniques that address the high dimensionality and spatio-temporal dependencies inherent to this data.

Methods for Trajectory Data Analysis
This research encompasses the development and application of analytical methods to understand movement patterns over space and time. Methods for analysis include trajectory classification, similarity measures, clustering, segmentation, and summarization, with applications in transportation, urban planning, and behavioral analysis.

Artificial Intelligence for Mobility Data Analysis
This topic involves the application of AI techniques to analyze and interpret mobility data, machine learning and large language models for analysis, behavior understanding and pattern discovery.