Seminar Assignment Winter 2024/2025

The central registration for all computer science seminars will open on September 11th.

This system is used to distribute students among the available seminars offered by the CS department. To register for any of the seminars, you have to register here until October 16th, 23:59 CET. You can select which seminar you would like to take, and will then be automatically assigned to one of them on October 18th.

Please note the following:

  • We aim to provide a fair mapping that respects your wishes, but at the same time also respects the preferences of your fellow students.
  • Experience has shown that particular seminars are more popular than others, yet these seminars cannot fit all students.
  • Please only select seminars if you are certain that you actually do want to complete a seminar this semester. If you have already obtained sufficient seminar credits, or plan to take other courses this semester, feel free not to choose any seminars. Students who drop out of seminars take away places from those, who might urgently need a space or are strongly interested in the topic.
  • We encourage those students who wish to take a seminar this semester, to select their preferences for all available seminars, which eases the process to assign students that do not fit the overly popular seminars to another, less crowded one. So if you are serious about taking a seminar this semester, please select at least three seminars (with priority from "High" to "Low").
  • If you urgently need to be assigned to a seminar in the upcoming semester, choose at least five seminars (with priority from "High" to "Low"). We will then guarantee that you will be assigned to a seminar (yet not necessarily one of your choice).
  • If you are really dedicated to one particular seminar, and you do not want any other seminar, please select the "No seminar" as second and third positive option. However, this may ultimately lead to the situation that you are not assigned to any seminar. Also, choosing "No seminar" as second/third option does not increase your chances of getting your first choice.

The assignment will be performed by a constraint solver on October 18th, 2024. You will be added to the respective seminars automatically and be notified about this shortly thereafter. Please note that the assignment cannot be optimal for all students if you drop the assigned seminar, i.e., make only serious choices to avoid penalty to others.


Aspects of Sustainable Computer Systems by Andreas Schmidt, Robin Ohs, BOSS Team Ruhr-Universit├Ąt Bochum

A multi-site seminar on "how to make computer systems sustainable".

As the world struggles with climate change, the computing sector's role as a source of greenhouse emissions (and other bad environmental impact) is steadily increasing. This seminar, co-located and co-hosted with the BOSS Team at Ruhr-Universit├Ąt Bochum (RUB), explores the intersection of computer technology and sustainability - identifying the key challenges of and innovative solutions for a sustainable future with computers.

We begin by looking at the origins of carbon emissions and learning to understand where emissions are caused. We then focus on known and new methods for measuring and modeling emissions in computer systems. Finally, we show ways in which systems and software can be adapted to become more sustainable.

More details here:

Requirements: - Open for advanced Bachelor and any Master students.
- Ideally, you have taken courses from the "systems & software" domain: e.g. Data Networks, Operating Systems, Distributed Systems, Software Engineering.

Places: 8

Exploring Explainability in Machine Learning by Holger Hermanns, Hanwei Zhang

This seminar course delves into the crucial and evolving field of explainability in machine learning (ML). As ML models become increasingly complex and integral to various domains, understanding how these models make decisions is essential. This course will explore different methodologies for interpreting ML models, including rule-based, attribution-based, example-based, prototype-based, hidden semantics-based, and counterfactual-based approaches. Through a combination of paper readings, discussions, and presentations, students will gain a comprehensive understanding of the challenges and advancements in making ML models transparent and interpretable.

More details will be released at

Requirements: The student should take a course in machine learning or have sufficient knowledge from other courses; The student should speak English and understand that the seminar will be conducted entirely in English

Places: 8

Hot Topics in Data Networks by Anja Feldmann

While the Internet has started as a research effort, it has consistently evolved throughout the decades into the largest commercial network. Today, many research efforts focus on understanding the Internet's structural trends, optimizing its packet delivery, and laying the foundations for its future developments. In this seminar, you will receive a closer look at bleeding-edge research published at the top conferences in our domain. Discussing state-of-the-art approaches and recent findings from a broad range of network-related topics with your peers and instructors will provide you with a deep understanding of your assigned topic. Preparing the accompanying research survey and topic presentation will not only strengthen your academic reading and writing skills but also help you to present your future work in a more accessible and structured way.

Requirements: Participants should have successfully participated in "Data Networks" or an equivalent course.

Places: 12

Machine Learning for Natural Language Processing by Dietrich Klakow

Deep learning is the predominant machine learning paradigm in natural language processing (NLP). This approach not only gave huge performance improvements across a large variety of natural language processing tasks.

For more information see:

Places: 8

Privacy of Machine Learning by Yang Zhang

Machine learning has witnessed tremendous progress during the past decade, and data is the key to such success. However, in many cases, machine learning models are trained on sensitive data, e.g., biomedical records, and such data can be leaked from trained machine learning models. In this seminar, we will cover the newest research papers in this direction.

Requirements: Students are required to have basic knowledge of machine learning.

Places: 15

Router lab by Tobias Fiebig,

In this seminar, we take a hands-on approach to explore the workings of internet components and provide a deep understanding of the underlying protocols. Specifically, we examine how routers and switches can be configured and connected to a working IP network.

The seminar consists of a series of activities starting with setting up the hardware and introducing basic configuration for VLANs and IPv4/IPv6 networks and gradually moving to more advanced topics, including but not limited to, routing protocols, virtual private networks, security, network monitoring, and troubleshooting.

Requirements: Participants should have sufficient knowledge of computer networks and how the Internet works, e.g., by having successfully participated in "Data Networks", an equivalent course, or by demonstrating relevant prior practical experience.

Places: 12