In this (german-language) episode of Code for Thought, Carina Haupt (DLR) and I talk with host Peter Schmid about large language models in Research Software Engineering: where tools like Copilot genuinely help (routine tasks, tests, small scripts), and where real-world projects expose their limits (builds, requirements, missing project context). I share my current work on matching papers to repositories via embeddings at the project level rather than just functions - so models carry meaningful context across an entire codebase.
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The Siddata project developed a digital study assistant that helps students find relevant courses, learning resources, and study partners using personalized recommendations. I developed machine learning recommenders and worked extensively on its Django backend, turning a research prototype that previously took days to deploy into a containerized, tested, and easily deployable system. My masters thesis was also conducted in the scope of this project.
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For this thesis, I created a Conceptual Space from Course Descriptions for explainable Recommendation, in a highly performant pipeline on the university-grid.
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As a student research assistant at the Red Hen Lab, I worked on a video-processing pipeline for automatic gesture analysis in large video corpora. My work involved person tracking and identification using face recognition, shot segmentation, and deploying the pipeline on an HPC cluster using Snakemake and Singularity.
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A speech-controlled meditation assistant and sentiment tracker
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Delving into the realm of self-driving cars for my bachelor’s thesis, I endeavored to optimize their tactical decisions, particularly speed, using cutting-edge deep neural networks and reinforcement learning in tensorflow within a transformed racing simulation. The resulting research platform, built on Unity, showcased the potential of real-time feedback and adaptability, offering valuable insights into the future integration of reinforcement learning in self-driving cars.
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