Tudor Lapusan
Co-author & Contributor DTreeViz (Open Source Project) at Cyber Threat Defense
Cluj-Napoca, Cluj, Romania
Summary
🤩
Rockstar🎓
Top SchoolTudor Lapusan is a hands-on machine learning and big data engineer with 11 years of experience building scalable data platforms, ML systems, and production inference pipelines, currently leading ML efforts in AI-driven cybersecurity. He co-authors and maintains DTreeViz, a globally used open-source library for decision-tree interpretability, contributing visualization features and tests that make complex models more transparent. Tudor blends deep data engineering (Spark, Hadoop, Snowflake) with practical MLOps on AWS, and applies transformer-based models and structured prompting for explainable security workflows. A founder of the Cluj-Napoca Big Data & Data Science community and an early entrepreneur who built a visual search startup, he pairs product intuition with technical rigor. Outside of engineering he organizes local meetups and even crafts beer at home, reflecting a curious, experimental mindset that fuels his work.
10 years of coding experience
1 year of employment as a software developer
Degree in computer science, Degree in computer science at Babeş-Bolyai University – Faculty of Mathematics and ComputerHigh school graduation, High school graduation at School of Computer Science and Electrical Group BistritaBabes-Bolyai Master- Distributed Systems on Internet
Stackoverflow
173reputation
29kreached
9answers
3questions
Github Skills (13)
data-visualizations10
decision-tree10
machine-learning10
data-visualization10
data-visualisation10
python10
apache-spark6
regression6
jupyter-notebook6
random-forest6
plot6
runnable6
graphviz6
Programming languages (3)
Github contributions (5)
parrt/dtreeviz
Sep 2019 - Jan 2023
A python library for decision tree visualization and model interpretation.
Role in this project:
Data Scientist
Data ScientistContributions:35 reviews, 196 commits, 47 PRs in 3 years 4 months
Contributions summary:Tudor's commits focused on adding visualizations for leaf samples in decision tree models. This included implementing visualizations for leaf criterion and number of samples. They also added tests to verify the implementations and created a template for a new model that supports more visualization capabilities. The user was also adding documentation.
xgboostdecision-treemodel-interpretationpythonrandom-forest
tlapusan/dtreeviz
Oct 2019 - Dec 2022
A python library for decision tree visualization and model interpretation.
Contributions:33 commits, 168 pushes, 45 branches in 3 years 2 months
decision-treepython-librarymodel-interpretationpythontree-visualization
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