Marco Pegoraro
Scientific Assistant at RWTH Aachen University
Aachen, North Rhine-Westphalia, Germany
Summary
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Senior🎓
Top SchoolMarco Pegoraro is a research-focused Scientific Assistant at RWTH Aachen with nine years of experience bridging process mining, data science, and AI. Currently pursuing a doctorate in the renowned PADS group led by Wil van der Aalst, he specializes in process mining for uncertain settings and healthcare applications while also engaging with NLP, machine learning, and information ethics. He has a strong track record of applied R&D—from contributing core algorithms and debugging reachability graphs in the popular PM4Py open-source library to prototyping document and OCR solutions in industry. Past roles include EU-funded ICU4Covid research and an AI internship at NASA Ames, reflecting a blend of academic rigor and practical engineering. Marco’s work combines a taste for conceptual complexity with an insistence on practical, uncluttered solutions.
9 years of coding experience
4 years of employment as a software developer
Master of Science (M.Sc.), Artificial Intelligence, Master of Science (M.Sc.), Artificial Intelligence at Università degli Studi di Padova
English, Italian
Stackoverflow
1reputation
0reached
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Github Skills (6)
algorithms10
python10
data-science9
data-structure9
data-structures9
machine-learning8
Programming languages (3)
Github contributions (5)
process-intelligence-solutions/pm4py
Aug 2018 - Oct 2019
Official public repository for PM4Py (Process Mining for Python) — an open-source library for exploring, analyzing, and optimizing business processes with Python.
Role in this project:
Data Scientist
Data ScientistContributions:32 commits, 5 PRs, 13 pushes in 1 year 2 months
Contributions summary:Marco primarily contributed to the development and debugging of process mining algorithms within the PM4Py library. Their work included implementing and refining reachability graph construction, a core component for analyzing Petri nets. They also added functionality for extracting process variants, essential for understanding and comparing different process execution paths. Furthermore, the user corrected bugs in the marking representation and tested the developed algorithms.
public-repositorypythonminingpm4pydata-science
proved-py/proved-core
Aug 2019 - Jan 2023
PROVED (PRocess mining OVer uncErtain Data) is a library of functionalities to perform process mining on uncertain event data.
Contributions:1 review, 253 commits, 3 PRs in 3 years 5 months
event-dataminingprocess-miningdata-science
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