piwik-script

Intern
Lehrstuhl für Informatik III

Teaching

Telefon (0931) 31-88150
Telefax (0931) 31-86632
E-Mail nikolas.wehner@informatik.uni-wuerzburg.de
Raum B208
Anschrift Lehrstuhl für Informatik III
Am Hubland
D-97074 Würzburg

Open Theses and Student Projects

Supervised Theses

  • Rduch, T. Investigation of Fundamental Differences in the Perception of Web Browsing and Video Streaming Quality of Experience [Master thesis]. University of Würzburg.
  • Pham, T. T. Quantifying the Influence of Consent Banners on Web QoE with Crowdsourcing [Master thesis]. University of Würzburg.
  • Amir, M. Relating Google’s Web Vitals and Web QoE in a Crowdsourcing Approach [Master thesis]. University of Würzburg.
  • Frieling, M. Evaluating the Interaction between Web and Video QoE with Crowdsourcing [Master thesis]. University of Würzburg.
  • Engelbrecht, T. Investigating the Relationship of Network Data Arrival and the Rendering Process in Chrome [Master thesis]. University of Würzburg.
  • Oesen, E. Machine Learning Based Web QoE Monitoring for Encrypted Network Traffic [Master thesis]. University of Würzburg.
  • Arslanian, A. QoE Study for Compatible Video and Web Browsing QoE Models [Master thesis]. University of Würzburg.
  • Hofmann, J. Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking [Master thesis]. University of Würzburg.
  • Berani, S. Fingerprinting Websites in Encrypted Network Traffic [Master thesis]. University of Würzburg.
  • Fehler, H. Comparison of Web QoE Algorithms on Different Devices [Master thesis]. University of Würzburg.
  • Mertinat, N. Impact of Content Selection on Crowdsourced QoE Studies of HTTP Adaptive Streaming on Mobile Devices [Master thesis]. University of Würzburg.

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)