Rosaria Silipo
Coordinatrice Didattica
Constance, Baden-Württemberg, Germany
Summary
🤩
Rockstar🎓
Top SchoolRosaria Silipo is an interdisciplinary AI and data science leader with 11+ years applying machine learning across biomedical systems, IoT, finance, cybersecurity, and speech processing. She combines deep technical chops—C#, C++, SQL, KNIME and hands-on model work including contributions to the Julia MLJ framework—with experience designing and QA-ing AI education programs now run from Constance/Zürich. At KNIME she scaled evangelism and university partnerships, hired and mentored junior data scientists, and translated complex analytics into teachable, production-ready workflows. A PhD in Bioengineering and a background in speech recognition and automatic translation give her a research-first lens that she pairs with practical product and reporting skills. Beyond coding and teaching, she’s a practiced data storyteller and communicator who turns models into clear business insights and learning paths.
11 years of coding experience
23 years of employment as a software developer
Massachusetts Institute of TechnologyAncient/Classical Greek Language and Literature, Ancient/Classical Greek Language and Literature at Liceo Galileo Galilei, Firenze, ItalyPhD, Bioengineering, PhD, Bioengineering at Università degli Studi di Firenze
English, German, Italian
Stackoverflow
21reputation
1kreached
2answers
0questions
Github Skills (12)
regression10
machine-learning10
julia10
pipeline9
classification9
data-science8
ensemble-learning7
data-mining6
data-visualization6
time-series6
r6
knime6
Programming languages (5)
Github contributions (5)
JuliaAI/MLJ.jl
Aug 2018 - Oct 2020
A Julia machine learning framework
Role in this project:
Data Scientist
Data ScientistContributions:50 commits, 9 PRs, 34 pushes in 2 years 2 months
Contributions summary:Rosaria primarily contributed to refactoring and adapting the MLJ framework to use new types. The user replaced "Task" with "MLTask", "MLR" with "MLJ", and made changes to the `MLJModel` structure. They also modified several wrapper files related to specific models like Ridge, Lasso, and Elasticnet and implemented new methods for predicting and learning using these models. They updated the script to include example usage of the models.
framework-learningensemble-learningclassificationstatisticspipeline
darenasc/aeda
Jun 2021 - Jan 2023
Build a data catalog by running a single call with reading privileges
Contributions:96 commits, 9 PRs, 59 pushes in 1 year 6 months
data-explorationedametadata-extractiondata-catalogdatabase
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