Professor Moe Thandar Wynn
Faculty of Science,
School of Information Systems
Biography
Research theme: Information;Research discipline: Information Systems
Researcher Profile:
- Awarded a PhD on the topic of "Semantics, Verification, and Implementation of Workflows with Cancellation Regions and OR-joins" from QUT in Nov 2006
- Published 110+ refereed research papers including 40+ journal articles, 40+ refereed conference papers.
- Attracted external funds for QUT in excess of AUD $5 million as a chief investigator across 22 research programs since 2011
- Google h-index: 36, 6000+ citations; Scopus h-index: 27, 3000+ citations (March 2023)
- Vice-chair and a steering committee member within IEEE task Force on Process Mining
- Co-leader, Data for Discovery Theme, QUT's Tier 1 Centre for Data Science
- Research interests: Process Mining, Process Automation, Data Quality, Robotic process automation, Business process analytics (simulation, monitoring, mining), Workflow patterns and Yet Another Workflow Language, Petri nets and Reset nets
As an international BPM researcher and educator, Prof Wynn has served as a co-chair for conferences and workshops, program committee member for international conferences, grant assessor for the ARC research council, PhD thesis examiner, and a reviewer of international journals. She was a program committee chair of the 2nd International Conference on Process Mining 2020 and the International Conference on Business Process Management 2021 and invited as an expert member to contribute to the CRC Food Agility: Mission Food for Life 2020 project. She was a co-editor of a special Issue on Robotic Process Automation (RPA) in the Computers in Industry Journal (2021) and is a co-editor of a special Issue on Managing the Dynamics of Business Processes in the Business & Information Systems Engineering journal (2023).
Prof Wynn was recognised as a recipient of QUT Vice-Chancellor’s Excellence Award (individual) for exceptional sustained performance and outstanding achievement in two categories (Research, Partnerships and Engagement), 2018 and a recipient of QUT Vice-Chancellor’s Excellence Award (individual) for research excellence in 2022.
Personal details
Positions
- Professor
Faculty of Science,
School of Information Systems
Keywords
Business Process Management, Process Oriented Data Mining, Process Mining, Data Quality, (Robotic) Process Automation, Data-driven Decision Making, Process Science, Process Analytics, Process Intelligence
Research field
Information Systems
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008
Qualifications
- Doctor of Philosophy (Queensland University of Technology)
- Master of Information Technology (Research) (Queensland University of Technology)
Professional memberships and associations
- I am vice-chair and steering committee member of the IEEE Task force on Process Mining.
- I am a member of Women in Technology (WiT).
- I am a member of the IEEE society.
- I am a senior fellow of Higher Education Academy (SFHEA).
Teaching
Teaching discipline: Information Systems
Teaching interests:
- Business process modelling
- Business process automation
- Business process analytics
- Workflow management
- Information systems
- Database technologies
2020 - 2023 Semester 1 Teaching
- IFN650 Business Process Analytics (Unit Coordinator and Lecturer)
2016-2019 Teaching
- IFN650 Business Process Analytics (Unit Coordinator and Lecturer/Tutor)
- IAB321 Business Process Technologies (Unit Coordinator and Lecturer/Tutor)
Experience
Prof Wynn has extensive knowledge of information systems, data architecture, data quality, workflow technologies and process-oriented data mining. She has over fifteen years of experience in conducting applied research across multiple Australian sectors engaging with logistics, healthcare, insurance, utility, education, government, mining, and agri-food supply chains to pinpoint inefficiencies and derive data-driven improvements
Publications
- Syed, R., Eden, R., Makasi, T., Chukwudi, I., Mamudu, A., Kamalpour, M., Kapugama Geeganage, D., Sadeghianasl, S., Leemans, S., Goel, K., Andrews, R., Wynn, M., ter Hofstede, A. & Myers, T. (2023). Digital Health Data Quality Issues: Systematic Review. Journal of Medical Internet Research, 25. https://eprints.qut.edu.au/239040
- Fehrer, T., Fischer, D., Leemans, S., Röglinger, M. & Wynn, M. (2022). An assisted approach to business process redesign. Decision Support Systems, 156. https://eprints.qut.edu.au/228761
- Goel, K., Leemans, S., Martin, N. & Wynn, M. (2022). Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations. ACM Transactions on Knowledge Discovery from Data, 16(5). https://eprints.qut.edu.au/228478
- Wynn, M., Poppe, E., Xu, J., ter Hofstede, A., Brown, R., Pini, A. & van der Aalst, W. (2017). ProcessProfiler3D: A visualisation framework for log-based process performance comparison. Decision Support Systems, 100, 93–108. https://eprints.qut.edu.au/105525
- Fischer, D., Goel, K., Andrews, R., van Dun, C., Wynn, M. & Röglinger, M. (2022). Towards Interactive Event Log Forensics: Detecting and Quantifying Timestamp Imperfections. Information Systems, 109. https://eprints.qut.edu.au/229352
- Andrews, R., van Dun, C., Wynn, M., Kratsch, W., Roglinger, M. & ter Hofstede, A. (2020). Quality-informed semi-automated event log generation for process mining. Decision Support Systems, 132. https://eprints.qut.edu.au/180260
- Suriadi, S., Andrews, R., ter Hofstede, A. & Wynn, M. (2017). Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs. Information Systems, 64, 132–150. https://eprints.qut.edu.au/97670
- Pika, A., Leyer, M., Wynn, M., Fidge, C., ter Hofstede, A. & van der Aalst, W. (2017). Mining resource profiles from event logs. ACM Transactions on Management Information Systems, 8(1). https://eprints.qut.edu.au/80195
- Andrews, R., Goel, K., Corry, P., Burdett, R., Wynn, M. & Callow, D. (2022). Process Data Analytics for Hospital Case-mix Planning. Journal of Biomedical Informatics, 129. https://eprints.qut.edu.au/228885
- Pika, A., van der Aalst, W., Wynn, M., Fidge, C. & ter Hofstede, A. (2016). Evaluating and predicting overall process risk using event logs. Information Sciences, 352 - 353, 98–120. https://eprints.qut.edu.au/85441
QUT ePrints
For more publications by Moe Thandar, explore their research in QUT ePrints (our digital repository).
Awards
- Type
- Membership of Review Panels on Prestigious Grant Applications
- Reference year
- 2023
- Details
- Member of the ARC College of Experts (2023 - 2025)
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2022
- Details
- QUT Vice-Chancellor's Excellence Award (Research)
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2019
- Details
- I am a recipient of QUT Vice-Chancellor's Excellence Award (individual) for exceptional sustained performance and outstanding achievement in two categories (Research, Partnerships and Engagement) in 2018.
- Type
- Advisor/Consultant for Industry
- Reference year
- 2019
- Details
- I am a pioneer in delivering impactful outcomes using research-informed process-oriented data mining (process mining) techniques for Australian industry stakeholders. I have already supported Australian stakeholders, in a range of sectors including healthcare, insurance, utility, retail, government and education to pinpoint inefficiencies and derive concrete performance improvements.
- Type
- Other
- Reference year
- 2019
- Details
- I am a steering committee member of the IEEE Taskforce on Process Mining. I am a working group member of the IEEE standardisation of eXtensible Event Stream (XES) and involved in approving XES certification of commercial and open-source process mining tools.
- Type
- Other
- Reference year
- 2013
- Details
- I hold a joint US patent with SAP on the Evaluation of Synchronization Gateways in Process Models (Filed: Nov 7, 2007, Patent# 8418178 (02007P00465US), Date: April 9, 2013). Inventors: Marlon G. Dumas, Alexander Grosskopf, Thomas Hettel and Moe T. Wynn.
Selected research projects
- Title
- Mathematical Decision Support to Optimise Hospital Capacity and Utilisation
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- LP180100542
- Start year
- 2020
- Keywords
- Title
- Improved Business Decision-Making via Liquid Process Model Collections
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP150103356
- Start year
- 2015
- Keywords
- process mining; process model collection; business process management
- Title
- Cost-aware business process management
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP120101624
- Start year
- 2012
- Keywords
- Information System; Business Process Model; Workflow Management; Management Accounting
- Title
- Reducing variation in clinical practice: a twin track approach to support improved performance
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- Start year
- 2012
- Keywords
- Clinical Practice; Cost-Effectiveness; Performance; Process Mining
- Title
- Risk-Aware Business Process Management
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP110100091
- Start year
- 2011
- Keywords
- Risk Management; Information System; Business Process Model; Workflow Management
Projects listed above are funded by Australian Competitive Grants. Projects funded from other sources are not listed due to confidentiality agreements.
Supervision
Current supervisions
- Process Mining with Exogenous Data
PhD, Principal Supervisor
Other supervisors: Dr Sander Leemans, Dr Robert Andrews - Visual Support for Event Log Imperfection Pattern Detection and Repair
PhD, Associate Supervisor
Other supervisors: Professor Arthur ter Hofstede - Quality Drift in Process Event Streams
PhD, Principal Supervisor
Other supervisors: Professor Arthur ter Hofstede - A Conceptualisation of Process Mining Impacts
PhD, Principal Supervisor
Other supervisors: Dr Sander Leemans, Associate Professor Wasana Bandara - Unveiling Dynamics Between Robotic Process Automation and Process Knowledge Loss
PhD, Mentoring Supervisor
Other supervisors: Dr Rehan Syed
Completed supervisions (Doctorate)
- Process mining with labelled stochastic nets (2024)
- Advancing process analytics for agri-food supply chains (2023)
- The Quality Guardian: Improving Activity Label Quality in Event Logs Through Gamification (2022)
- Influence of Performance Measurements on Institutionalizing Process Improvement Initiatives (2019)
- Towards Cost Model-driven Log-based Business Process Improvement (2016)
- Evaluating Business Process Compliance Management Frameworks (2015)
- Mining Process Risks and Resource Profiles (2015)