Daniel Ruffinelli
Postdoctoral Researcher at Universität Mannheim
Mannheim, Baden-Württemberg, Germany
Summary
👤
Senior🎓
Top SchoolDaniel Ruffinelli is a Postdoctoral Researcher in NLP at the University of Mannheim with 14 years of software and research experience, specializing in interpretability of large language models and representation learning for structured data. He holds a PhD in Machine Learning and built core components for LibKGE, a reproducible PyTorch knowledge graph embedding library, translating research ideas into production-grade code and large-scale GPU training pipelines. Daniel teaches and designed MSc courses on Advanced NLP and Information Retrieval, supervises PhD students to publications at top NLP venues, and blends strong pedagogy with hands-on model development. His background ranges from ERP and RESTful systems development to teaching maths and English, reflecting an unusual mix of applied engineering, formal reasoning, and multilingual communication that aids cross-disciplinary collaboration.
14 years of coding experience
6 years of employment as a software developer
Electronics Engineering, Electrical and Electronics Engineering, Electronics Engineering, Electrical and Electronics Engineering at National University of Asunción, ParaguayHigh school Diploma, High school Diploma at Goethe SchuleComputer Science Diploma, Computer Science, Computer Science Diploma, Computer Science at Catholic University of AsunciónDoctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at University of MannheimEngineering Degree, Engineering Degree at The Catholic University of Asunción
English, Spanish, german (telc b1)
Github Skills (19)
pytorch10
python10
wikipedia10
machine-learning10
scala10
graph-embedding10
knowledge-graph10
data-extraction10
model-driven9
configuration-management9
data-preprocessing9
modeling9
model-driven-development9
model-building9
rdf8
Programming languages (5)
Github contributions (5)
uma-pi1/kge
Feb 2019 - Dec 2022
LibKGE - A knowledge graph embedding library for reproducible research
Role in this project:
Back-end Developer
Back-end DeveloperContributions:12 reviews, 157 commits, 5 PRs in 3 years 9 months
Contributions summary:Daniel appears to be focused on the development of a knowledge graph embedding library. Their contributions involve creating the core components of the library such as data views, models, experiments, and evaluation tools. The user implemented foundational classes and interfaces like `BaseDataView`, `BaseModel`, `BaseExperiment`, and `BaseEvaluator`. Additionally, they worked on downloading and preprocessing datasets.
knowledgereproducibleknowledge-graphknowledge-graph-embeddinggraph
dbpedia/extraction-framework
May 2014 - Nov 2014
The software used to extract structured data from Wikipedia
Role in this project:Back-end Developer
Back-end DeveloperContributions:136 commits in 6 months
Contributions summary:Daniel's contributions primarily focused on modifying configuration files and code related to data extraction from Wikipedia dumps. They updated default file names, and integrated changes from the upstream master branch. These changes involved modifying Scala code, which suggests a focus on the core logic of the data extraction framework. Further modifications included adjustments to the processing of page titles and RDF namespaces, demonstrating a strong understanding of the system's inner workings.
pythonstructured-datastructuredwikipedia
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