I’m Gyunam Park, a Ph.D. candidate and computer science researcher specializing in process mining at the chair of Process and Data Science (PADS), which is led by Prof. Dr. Wil M.P. van der Aalst. I am deeply passionate about designing/developing/implementing algorithms and methods to analyze event data in order to understand the underlying processes, e.g., healthcare, education, manufacturing processes. My research primarily revolves around object-centric process mining, which involves analyzing the interactions between objects or entities within processes, and action-oriented process mining, wherein the insights derived from object-centric process mining are translated into actions. Check out Google Scholar for my recent publications.DOWNLOAD CV
Undergraduate and Graduate Course at Computer Science, RWTH-Aachen University (04.2022 ~ Ongoing)
Undergraduate and Graduate Course at Computer Science, RWTH-Aachen University (04.2021 ~ 10.2021)
Undergraduate Course at Computer Science, RWTH-Aachen University (04.2020 ~ 03.2022)
This project aims to introduce AI-driven resources and assessments to enhance the teaching and learning experiences for students and curriculum planners. The AI-enabled ‘study companion’ tool offers targeted assistance to students, like setting objectives to secure top grades in a course and generating suggestions to meet those objectives. The AI-integrated ‘companion analytics’ tool equips curriculum planners with dashboards and assists them in the process of (re)structuring study programs.
The initial and most costly phase in process mining involves retrieving, converting, and uploading event logs from information systems. Specifically, pulling event data from prevalent ERP platforms like SAP is a significant hurdle due to the data’s magnitude and organization. The purpose of this project is to first obtain object-focused event data from SAP ERP platforms, and then uncover and examine both familiar and unfamiliar processes within these systems.
The importance of efficiently operating and managing manufacturing equipment in a process is highlighted in this research. By utilizing process mining techniques, analysis of equipment status and operation status becomes possible. The research aims to develop an equipment mining algorithm for deriving the Best Reference equipment. The study involves analyzing core semiconductor manufacturing processes, identifying problem equipment, engaging in process mining technology sensing activities that include technology and case introductions, and developing a methodology for identifying the Best Reference equipment using process mining techniques.