Cognitive Science on cstenkamp.de

Podcast Episode on LLMs for Software Engineering

cstenkamp@gmail.com (Chris Stenkamp) — Mon, 26 May 2025 00:00:00 +0000

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.

Siddata Study Assistant

cstenkamp@gmail.com (Chris Stenkamp) — Mon, 16 May 2022 00:00:00 +0000

The Siddata-Project facilitates study individualization through a digital data-driven assistant, available as a Stud.IP Plugin.

Masterthesis: Explainable Course Recommendation

cstenkamp@gmail.com (Chris Stenkamp) — Thu, 14 Apr 2022 00:00:00 +0000

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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Gesture Recognition in Large Video Corpora

cstenkamp@gmail.com (Chris Stenkamp) — Wed, 17 Mar 2021 00:00:00 +0000

In this student research job for the Red Hen Lab, I worked on a pipeline that automatically detects hand gestures in large datasets of videos, involving OpenPose for Pose Recognition, Person detection, Person tracking, Scene detection and more, and deployed it on a HPC using Snakemake and Singularity to create a multimodal communication corpus from the Ellen DeGeneres show.

Chatbot: Guru To Go

cstenkamp@gmail.com (Chris Stenkamp) — Thu, 05 Mar 2020 00:00:00 +0000

A speech-controlled meditation assistant and sentiment tracker

Bachelorthesis: Controlling Race Cars with Deep ANNs

cstenkamp@gmail.com (Chris Stenkamp) — Thu, 14 Sep 2017 00:00:00 +0000

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.