Academic Staff
Tobias King
Profile
Exploring design automation and optimization for wearable computing systems
Short CV
- 2019 – 2022 Master degree in Computer Science at KIT
- 2015 – 2019 Bachelor degree in Computer Science at KIT
Research Interests
- TinyML
- Human Activity Recognition
- LLMs for EDA
Projects
edge-ml
edge-ml is an embedded-first machine learning framework designed to help developers build models for microcontrollers faster and more robustly. As a browser-based, end-to-end solution, it simplifies the entire ML pipeline into a few simple steps: recording data, labeling samples, training models, and deploying validated embedded machine learning directly on the edge.
MicroNAS
MicroNAS is a hardware-aware neural architecture search (HW-NAS) framework designed for time series classification on microcontrollers (MCUs). It automatically generates efficient neural networks that meet strict memory and latency constraints, enabling real-time machine learning directly on low-power embedded devices.
OpenEarable
OpenEarable is an open-source, AI-enabled platform for ear-based sensing applications with true wireless audio. The modular and reconfigurable platform is packed with a variety of high-precision sensors, designed for both development and research applications.
Topic Areas
Publications
Burzer, M.; King, T.; Riedel, T.; Beigl, M.; Röddiger, T.
2025. Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 1315–1322, Association for Computing Machinery (ACM). doi:10.1145/3714394.3756254
Röddiger, T.; Zitz, V.; Hummel, J.; Küttner, M.; Lepold, P.; King, T.; Paradiso, J. A.; Clarke, C.; Beigl, M.
2025. CHI EA ’25: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Art.-Nr.: 713, Association for Computing Machinery (ACM). doi:10.1145/3706599.3721161
King, T.; Zhou, Y.; Röddiger, T.; Beigl, M.
2025. Scientific Reports, 15 (1), Art.-Nr.: 7575. doi:10.1038/s41598-025-90764-z
Röddiger, T.; Küttner, M.; Lepold, P.; King, T.; Moschina, D.; Bagge, O.; Paradiso, J. A.; Clarke, C.; Beigl, M.
2025. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9 (1), Art.-Nr.: 16. doi:10.1145/3712069
King, T.; Knierim, M.; Lepold, P.; Clarke, C.; Gellersen, H.; Beigl, M.; Röddiger, T.
2025. Scientific Reports, 15 (1), 32437. doi:10.1038/s41598-025-16839-z
King, T.; Röddiger, T.; Laubenstein, D.; Beigl, M.
2024. Proceedings of the 2024 ACM International Symposium on Wearable Computers, 144–147, Association for Computing Machinery (ACM). doi:10.1145/3675095.3676627
Lepold, P.; Röddiger, T.; King, T.; Kunze, K.; Maurer, C.; Beigl, M.
2024. UbiComp ’24: Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, 916 – 920, Association for Computing Machinery (ACM). doi:10.1145/3675094.3678480
Zhou, Y.; King, T.; Zhao, H.; Huang, Y.; Riedel, T.; Beigl, M.
2024. ISWC ’24: Proceedings of the 2024 ACM International Symposium on Wearable Computers. Ed.: V. Kostakos, 133–139, Association for Computing Machinery (ACM). doi:10.1145/3675095.3676624
Zhou, Y.; King, T.; Huang, Y.; Zhao, H.; Riedel, T.; Röddiger, T.; Beigl, M.
2024. 22nd IEEE International Conference on Pervasive Computing and Communications (PerCom 2024), 33–38, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/PerComWorkshops59983.2024.10502894
Lepold, P.; Röddiger, T.; King, T.; Kunze, K.; Maurer, C.; Beigl, M.
2024. doi:10.48550/arXiv.2410.06533
Röddiger, T.; King, T.; Roodt, D. R.; Clarke, C.; Beigl, M.
2023. UbiComp/ISWC ’22 Adjunct: Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers, 246–251, Association for Computing Machinery (ACM). doi:10.1145/3544793.3563415
Röddiger, T.; King, T.; Roodt, D. R.; Clarke, C.; Beigl, M.
2022, September 15