Roy Jing Yang 杨靖
researcher, process mining, business process management, data science
I am a postdoctoral research fellow at Queensland University of Technology (QUT), Australia. I’m interested in knowledge discovery from process execution data to support evidence-based decisions on human workforce, collaboration between humans and AI agents, and advanced forms of process automation.
My current research focuses on mining organizational models from process execution data and applying them to workforce analytics [read more].
I’m also a key contributor to Foragecaster, a project that aims at developing an AI-powered planner system to support farmers’ decisions on forage, grazing livestock, and farm sustainability. I lead the research on data quality and explainability, where we develop systematic methods to evaluate key farm production data, address data quality issues, and investigate their root causes to improve future data management and ensure effective use of machine learning models in a data-centric way.
I’m a member of the Explainable Analytics for Machine Intelligence (XAMI) Lab at QUT.
Education
- Ph.D. (2023), Queensland University of Technology, Australia
Thesis title: Discovering Organizational Models from Event Logs for Workforce Analytics. Advisors: Chun Ouyang, Arthur ter Hofstede, and Wil van der Aalst.- An e-copy of my doctoral thesis can be downloaded via [this link]
- TL;DR 5-page “Extended Abstract” via [this link]
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Master’s degree in Computer Science and Technology (2019), Sun Yat-sen University, China
Thesis title: An Organizational Mining Method for Supporting Business Process Redesign (in Chinese). Advisor: Yang Yu. - Bachelor’s degree in Software Engineering (2016), Sun Yat-sen University, China
Honors and Awards
- Best BPM Dissertation Award 2024, International Conference on Business Process Management
- Outstanding Doctoral Thesis Award (ODTA) for 2023, QUT
- Graduate of Merit, Food Agility
What's new
| 2026/07 |
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| 2026/06 |
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| 2025/05 |
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