Alexander Dietmüller

Supervision

I supervise Bachelor-, Semester-, and Master Theses on the topics of learning from network traffic as well as data-driven decisions in networks. Theses on the first topic can range from data science and analysis to machine learning. Theses on the second topic include areas such as congestion control design and analysis, video streaming, and network automation.

Don’t hesitate to contact me if you are interested in a thesis in one of theses areas! Let me know what you are interested in, and don’t forget to mention your relevant skills, previous projects, and lectures.

Bio

I am a fifth year PhD student and my research is focused on combining learning and control theory with large communication networks such as the Internet. For me, this means: (i) how can we learn the interactions between traffic and networks; and (ii) how can ensure that the data-driven decisions are optimal, in particular at the tail?

In my most recent project, I am tackling the issue of keeping ML models up to date with a focus on tail performance. (Re-training) ML models over time is known as continual learning (CL), and common CL systems are mostly focused on delivering good performance on average. Yet in networking, tail performance is very important, and thus I set out to develop a system that reliably identifies and remembers rare samples to improve model performance at the tail.

Furthermore, I am investigating how we can efficiently learn general network dynamics. I am investigating whether we can leverage state-of-the-art ML architectures to generalize network dynamics, i.e. extracting general patterns from a variety of traces for future predictions.

In 2022, I worked as a research intern with the Measurement Lab team at Google, supervised by Phillipa Gill and Matt Mathis.

In the past, I was working on programmable packet scheduling together with Albert.

Finally, and with the help of other Master students, I also investigated various aspects of congestion control.

I received my Bachelor and Master degrees in Electrical Engineering and Information Technology from ETH Zürich.

Talks

Publications

Running Theses

Supervised Theses