I am a postdoctoral research fellow at Queensland University of Technology (QUT), Australia. I’m interested in discovering knowledge from process execution data to support improved decision-making, especially knowledge about (human) resources, and 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 involved in 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 framework and methods to evaluate key farm production data, diagnose and repair potential data quality issues, and investigate their root causes to improve future data management and ensure effective use of machine learning models from a data-centric perspective.

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”, advised by Chun Ouyang, Arthur ter Hofstede, and Wil van der Aalst.
  • An e-copy of my doctoral thesis can be download via [this link]
  • TL;DR 5-page “Extended Abstract” via [this link]

• Master 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), advised by Yang Yu.

• Bachelor 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