Data Analytics Laboratory conducts a variety of research for process mining, which aims to extract process-oriented knowledge from event logs collected by information systems. This includes various techniques, including process discovery, conformance checking, predictive process monitoring, and others. Our laboratory develops novel methods for process mining and its applications in diverse domains, including healthcare, education, manufacturing, finance, and others.
Data Analytics Laboratory conducts a variety of research for text mining, which aims to investigate valuable information and knowledge from large volumes of text data. This includes various techniques, including text classification, sentiment analysis, topic modeling, LLM-based approaches, and others. Our laboratory resolves industry challenges using text mining techniques and develops novel methods to bridge the gap between NLP and other domains.
DA-Lab studies AI and data-driven modeling for complex real-world systems. We develop robust methods for learning from heterogeneous data including temporal, multimodal, sensor, healthcare, and ecological data to enable smarter decision-making across diverse application domains. This means building systems that can predict outcomes, explain their reasoning, and adapt to new environments, ultimately bridging the gap between raw data and actionable intelligence. Our research spans predictive analytics, process-aware modeling, and trustworthy AI, connecting core AI methodologies with practical decision support.