Biography
Daniel Stolpmann received his B.Sc. degree in computer science and engineering from Hamburg University of Technology, Germany, for his thesis on network coding for aircraft communication in 2017. In 2019, he received the corresponding M.Sc. degree with distinction for his thesis on joint edge server placement and network planning for mobile edge computing. During his master studies, he worked as a student assistant at the Institute of Communication Networks (ComNets) in the REKOTRANS project. Since his graduation, he is a research fellow at the institute.
Research Topics
- Machine Learning for Communication Networks
- Congestion Control & Active Queue Management
- Network Coding
- Mobile Edge Computing
- Satellite Communication
Research Projects
- Machine Learning Assisted Communication for Future Internet Applications
- FlowEmu: Flow-Based Network Emulator
- Machine Learning for Communications in Aviation
- MECPlan: Entwicklung von optimierten Planungsverfahren für Mobile Edge Computing in Mobilfunknetzen
- REKOTRANS: Online Flight Data Recorder
Teaching
Supervised Student Theses
- Evaluation of a 10Base-T1S Multidrop Bus with Traffic Prioritization for Aircraft Cabin Networks
Master Thesis, December 2023 - Design and Analysis of Machine Learning-Based Channel Switching Strategies in IEEE 802.11 Systems
Master Thesis, June 2023 - Fairness Analysis of Coexisting Conventional and Reinforcement Learning-Based Congestion Control
Master Thesis, January 2023 - AI-Enabled Interactive Dataset: A Step Towards Proactive Radio Management
Master Thesis, November 2022 - Efficient Spectrum Sensing through Reinforcement Learning for Multichannel Medium Access Prediction in Coexisting Wireless Networks
Master Thesis, July 2022 - Combining Explicit Congestion Control with Network Coding for High Throughput and Low Latency Communication via Lossy Networks
Master Thesis, July 2022 - Performance Evaluation of LoRaWAN, NB-IoT and EnOcean based Sensor Networks in Office Environments
Bachelor Thesis, May 2022 - Implementation and Evaluation of Active Queue Management Algorithms in a User Space Network Emulator
Research Project and Seminar, March 2022 - Modelling of IEEE 802.11 for Network Emulation
Research Project and Seminar, October 2021 - Reinforcement Learning-based Channel Sensing for Multichannel Access Prediction in Wireless Networks
Research Project and Seminar, July 2021
Awards
- "Best Presentation Award" for talk on "In-Network Round-Trip Time Estimation for TCP Flows"
2nd Workshop "Machine Learning & Networking" (MaLeNe), Potsdam, September 2023
Publications