Marcello La Rosa
Papers
11
Total Citations
301
H-Index
8
About
Marcello La Rosa is a leading researcher at the intersection of process mining and robotic process automation (RPA), whose work has fundamentally shaped how organizations identify, analyze, and automate repetitive business tasks. His research pioneers the emerging field of "Robotic Process Mining," which applies process mining techniques to user interaction logs in order to uncover automatable routines hidden within everyday clerical workflows. His most cited work, "Robotic Process Mining: Vision and Challenges" (2020, 125 citations), laid out a compelling research agenda that has since galvanized the broader community around this topic. Beyond vision-setting, La Rosa has made substantial practical contributions, developing tools such as Action Logger and Robidium that enable the automated discovery and scripting of RPA routines directly from UI logs — bridging the gap between raw interaction data and deployable automation. His research addresses key challenges including routine segmentation, data transfer discovery, and multi-perspective process model discovery, collectively spanning over 300 citations across his RPA-focused publications alone. For students and practitioners alike, La Rosa's body of work represents an essential foundation for understanding how intelligent automation can be grounded in rigorous, data-driven process analysis.
Research Focus
Key Achievements
Top Papers
- 1Robotic Process Mining: Vision and Challenges125 citations · 2020
- 2Discovering Automatable Routines from User Interaction Logs41 citations · 2019
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- 4Multi-Perspective process model discovery for robotic process automation29 citations · 2018
- 5Action Logger: Enabling Process Mining for Robotic Process Automation16 citations · 2019
- 6Robotic Process Mining16 citations · 2022
- 7Discovering data transfer routines from user interaction logs15 citations · 2021
- 8Automated Discovery of Data Transformations for Robotic Process Automation13 citations · 2020
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