Yannik Käckenmeister

Building the backend infrastructure for intelligent document processing—including inter-service communication, caching, persistence, and API design.

Conducted research on sparse indexing structures generated by neural retrievers in RAG systems and designed embedding pruning and expansion methods to improve their efficiency and effectiveness. Concluded with a one-month transition period focused on project handover, while completing my master's degree.

Built upon my bachelor’s thesis research on neural 3D object detection by applying a thesis-developed point cloud generator to create synthetic underwater data for model training and evaluation; co-authored the resulting publication.

Built microservices to parse and analyze vehicle test drive data, complemented by a lightweight GUI for service interaction and result visualization.

Developed new features for the “4EM” modeling software and supported teaching as a tutor and by checking preliminary exam work.

Grade: very good | Thesis: "Sparse Index Structures in RAG Systems: Efficient Search and Embedding Strategies for Heterogeneous Data Sets"

Grade: very good | Thesis: "Identification, Evaluation and Adaption of Neural Network Based 3D Object Detection Methods for Underwater Environments"