Research Projects

This project aims to develop an AI-augmented process science framework that integrates model intelligence, behavioral reasoning, and operational adaptation for data-driven process innovation.

This project aims to develop an AI-based platform that integrates remote sensing data and citizen science data to transform biodiversity monitoring, with the ultimate goal of building an operational system at TRL 5 and demonstrating validated monitoring accuracy of over 85% compared with conventional field-based methods.

This project aims to develop an LLM-based framework for the automated and quantitative evaluation of research reliability in social science, enabling more transparent, standardized, and objective assessment of reproducibility, replicability, and robustness.

This project aims to develop an empirical framework for evaluating online review helpfulness through the lens of dual-process theory, by identifying multidimensional review attributes, testing their relationships with intuitive and analytical decision-making, and applying predictive and explainable AI models across diverse e-commerce platforms.

This project aims to develop a healthcare object-centric process mining framework and analytical tools based on the OMOP Common Data Model and deep learning, covering standardized clinical object-centric event log construction, multi-viewpoint process modeling, and AI-driven process prediction and monitoring.