Rainer Gemulla
Professor at University of Mannheim
Mannheim, Baden-Württemberg, Germany
Summary
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Rockstar🎓
Top SchoolRainer Gemulla is a Professor and head of the Chair of Data Analytics at the University of Mannheim with nine years of professional experience bridging academic research and practical software development. His background includes postdoctoral work at IBM Almaden and research at the Max Planck Institute for Informatics, and he holds a Ph.D. from TU Dresden. He combines expertise in scalable data and knowledge-graph systems with hands-on engineering, contributing to open-source projects such as LibKGE for reproducible knowledge graph embeddings and enhancements to developer tooling in Emacs. Rainer’s work spans back-end architecture, experiment reproducibility, and developer experience improvements—demonstrated by modular refactors, dataset tooling, and user-friendly configuration systems. Colleagues describe him as a researcher who ships production-ready code, turning advanced data-science ideas into usable libraries and editor integrations.
10 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at Technische Universität Dresden
Github Skills (14)
lisp10
configuration-management10
smarty10
pytorch10
refactoring10
python10
data-management10
refactor10
emacs-lisp10
data-set10
datasets10
ui-design9
graphical-user-interface9
user-interface9
Programming languages (8)
Github contributions (5)
uma-pi1/kge
Mar 2019 - Jan 2022
LibKGE - A knowledge graph embedding library for reproducible research
Role in this project:
Back-end Developer
Back-end DeveloperContributions:51 reviews, 562 commits, 52 PRs in 2 years 11 months
Contributions summary:Rainer primarily contributed to the development of a knowledge graph embedding library. Their work involved refactoring the project by moving source files into a modular "kge" module, including the implementation of various experiment types and models. They also updated the dataset download scripts to include several new datasets, enhancing the library's capabilities. Furthermore, the user implemented a more user-friendly configuration system for models.
knowledge-graphreproducible-research
jorgenschaefer/elpy
Sep 2017 - Nov 2019
Emacs Python Development Environment
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:49 commits, 16 PRs, 7 pushes in 2 years 1 month
Contributions summary:Rainer primarily worked on improving Elpy's interactive Python shell integration. Their contributions include refactoring and separating code for shell functionality, adding features such as code cell support and improved output echoing, and implementing keybindings for sending code fragments. They also addressed bugs and enhanced the user experience with features like flashing regions and improved handling of output.
emacspython-development
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