Teaching

Telefon | (0931) 31-88150 |
Telefax | (0931) 31-86632 |
nikolas.wehner@informatik.uni-wuerzburg.de | |
Raum | A208 |
Anschrift | Lehrstuhl für Informatik III Am Hubland D-97074 Würzburg |
Open Theses and Student Projects
Supervised Theses
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Investigation of Fundamental Differences in the Perception of Web Browsing and Video Streaming Quality of Experience PhD thesis, University of Würzburg. (2022, July).
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Quantifying the Influence of Consent Banners on Web QoE with Crowdsourcing PhD thesis, University of Würzburg. (2022, June).
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Evaluation of Graph-based Deep Reinforcement Learning for the TSN Per-Port Latency Assignment Optimization Problem PhD thesis, University of Würzburg. (2022, October).
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Relating Google’s Web Vitals and Web QoE in a Crowdsourcing Approach PhD thesis, University of Würzburg. (2022, April).
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Evaluating the Interaction between Web and Video QoE with Crowdsourcing PhD thesis, University of Würzburg. (2022, July).
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Investigating the Relationship of Network Data Arrival and the Rendering Process in Chrome PhD thesis, University of Würzburg. (2021).
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Machine Learning Based Web QoE Monitoring for Encrypted Network Traffic PhD thesis, University of Würzburg. (2021).
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QoE Study for Compatible Video and Web Browsing QoE Models PhD thesis, University of Würzburg. (2021).
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Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking PhD thesis, University of Würzburg. (2020, July).
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Fingerprinting Websites in Encrypted Network Traffic PhD thesis, University of Würzburg. (2020).
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Comparison of Web QoE Algorithms on Different Devices PhD thesis, University of Würzburg. (2020).
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Impact of Content Selection on Crowdsourced QoE Studies of HTTP Adaptive Streaming on Mobile Devices PhD thesis, University of Würzburg. (2020, February).
Lectures (Teaching)
- Rechnernetze und Informationsübertragung (WS20/21)
- Simulationstechnik zur Systemanalyse (SS 2022)
Seminar Talks (Tutor)
- Website Fingerprinting (WS 21/22)
- Application Fingerprinting (WS 21/22)
- QoE and UX/Usability (SS 21)
- QoE and Sustainability (SS 21)
- Reinforcement Learning for Rate Control/DASH (WS 20/21)
- Reinforcement Learning for Scheduling (WS 20/21)
- Internet Access in High Speed Trains (SS 20)
- QoE Monitoring of Internet Applications (WS 19/20)
- QoE Monitoring from Encrypted Internet Traffic (WS 19/20)