~derf / Daniel Friesel
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About Me

I am well-versed in embedded development, machine learning, product line engineering, research including documentation / publication / presentation of results, prototype and product development, and a bit of project management. In my spare time, I enjoy building useful hardware and software applications, ranging from low-level AVR Assembler and C++/Lua drivers for embedded peripherals to full-stack web applications for public transit users and commuters.

I studied computer science at TU Dortmund with a minor in electrical engineering; my master's thesis covered automating energy model generation for embedded peripherals. I am currently following up on this by researching performance models for embedded software product lines and energy models for embedded peripherals – and teaching students about related topics – at Osnabrück University.

My research topics include automated measurements with cheap off-the-shelf hardware, a novel machine learning algorithm specifically tailored towards energy models of embedded peripherals, and a toolchain for performance-aware configuration of kconfig-based software product lines. All of these have a strong focus on automation: users and system designers should be freed from as many tedious and repetitive manual tasks as possible. You can find details in the publication list and on my home page at the Embedded Software Systems Group.

During my time at Osnabrück University, I have been involved in a project for resource-efficient AI in agricultural machinery from the initial proposal and funding application to the completion of work packages within the project, and a commercial research project with a manufacturer thereof. I have designed a programming course from the ground up and taught it to students, managed lecture exercises, supervised Bachelor's theses, and supervised student assistants developing a React-based web application.


My private open-source projects are available on GitHub. The projects page lists a few samples. Scientific code and paper artifacts are available on the ESS GitLab.


You can reach me by E-Mail at d​f@fina​lr​ewind.org. If desired, you can use PGP key 781BB707 1C6BF648 EAEB08A1 100D5BFB 5166E005 for encryption.