Seminar Assignment Summer 2025
The central registration for all computer science seminars will open on March 23rd.
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 April 8th, 23:59 CET. You can select which seminar you would like to take, and will then be automatically assigned to one of them on April 11th.
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 April 11th, 2025. 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.
Seminars
Block Seminar Machine Learning for Natural Language Processing (Fall 2025) 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 and the specific theme of this semester see:
Places: 8
Coping with computational hardness: approximation, moderately exponential-time, and parameterized algorithms by Daniel Marx
Most optimization problems and combinatorial search problems are NP-hard, hence we do not expect to be able to find polynomial-time algorithms to solve exactly. But even for such problems, it is still possible to prove rigorous theoretical results that show the existence of algorithms that solve the problem more efficiently than naïve or brute force approaches. Approximation algorithms do not provide an optimal solution, but there is a provable bound on the quality of solution they find. The field of moderately exponential-time algorithms designs algorithms that searches a much smaller (but still exponential) solution space than brute force algorithms do. The running time of a parameterized algorithm is polynomial in the input size, but possibly exponential (or worse!) in some well-defined parameter of the input.
Designing algorithms of this form often requires deep insights into the nature of the problem and clever algorithmic/combinatorial ideas. In this seminar, we will read, present, and discuss research results with the goal of seeing how these algorithmic paradigms can be applied problems in different domains.
Requirements: A solid background in algorithms and some familiarity with concepts in optimization is necessary for this seminar, as well as a general affinity towards mathematical proofs.
Places: 10
Data-driven Understanding of the Disinformation Epidemic (DUDE) by Yang Zhang
Arguably, one of the greatest inventions of humanity is the Web. Despite the fact it revolutionized our lives, the Web has also introduced or amplified a set of several social issues like the spread of disinformation and hateful content to a large number of people.
In this seminar, we will look into research that focuses on extracting insights from large corpus of data with the goal to understand emerging socio-technical issues on the Web such as the dissemination of disinformation and hateful content. We will read, present, and discuss papers that follow a data-driven approach to analyze large-scale datasets across several axes to study the multi-faceted aspects of emerging issues like disinformation.
During this seminar, the participants will have the opportunity to learn about state-of-the-art techniques and tools that are used for large-scale processing, including, but not limited to, statistical techniques, machine learning, image analysis, and natural language processing techniques.
Requirements: There are no formal prerequisites for this seminar. Despite this fact, it will be helpful if the participants have a basic understanding of machine learning and data mining.
Places: 20
Defining and Measuring Abstract Concepts in NLP by Vagrant Gautam
NLP papers commonly use various abstract concepts like “interpretability,” “bias,” “reasoning,” “stereotypes,” and so on. Each subfield has a shared understanding of what these terms mean and how we should treat them, and this shared understanding is the basis on which datasets are built to evaluate these abilities, metrics are proposed to quantify them, and claims are made about systems. But what exactly do these terms mean? And, indeed, what should they mean, and how do we measure that? These questions are the focus of this seminar on defining and measuring abstract concepts in NLP.
In 2-week cycles, we will cover various concepts in NLP, reading papers that analyze or critique how a given concept is used, and then using this as a lens to read, discuss, and critique 2 or more recent NLP papers that use that concept. We will also try to reimagine how we would run these projects and write these papers in light of what we have learned.
For the reading list and more information on course requirements and grading, see https://www.lsv.uni-saarland.de/upcoming-courses/seminar-defining-and-measuring-abstract-concepts-in-nlp-summer-2025/
Places: 7
Equality Saturation by Sebastian Hack
Equality saturation is an emerging technique for program and query optimization developed in the programming language community. It performs term rewriting over an E-graph, a data structure that compactly represents a program space. In this seminar we look at applications of E-graphs and equality saturation for program optimization, equality saturation algorithms, and the theoretical foundations of equality saturation that are rooted in automated reasoning and database theory.
Requirements: Compiler Construction Core Lecture
Places: 10
Interpreting and Analyzing Neural Language Models by Xinting Huang, Michael Hahn
Despite their huge success, neural networks are still widely considered “black-boxes”. In this seminar, we will look into interpretability methods that aim to demystify these models. We will focus on post-hoc interpretability for transformer-based language models, and work on relatively young and burgeoning fields such as Mechanistic Interpretability, which focuses on reverse-engineering model components into human-understandable algorithms. We will read recent papers that involve a diverse set of techniques for interpreting the inner-workings of language models
See the course website for more: https://lacoco-lab.github.io/courses/interpreting-2025
If you register for the course, you may be directly admitted or waitlisted. Final decisions will be made by the end of the first week of Summer semester.
Requirements: Required: Background in machine learning.
Recommended: Background in natural language processing.
Places: 12
Neural Networks in Brains and Computers by Michael Hahn
We will look into the intersection of machine learning and neuroscience. The main target audience is students with a background in machine learning who are interested in learning about the brain and how it processes information, how artificial neural networks relate to real neurons, how machine learning can help understand the brain better, and how machine learning models may end up aligning with representations found in the brain. The focus will mostly be on vision and language. Thus, this seminar may be particularly interesting if you have a background either in NLP/Computational Linguistics or in Computer Vision. Background in one of the two is certainly enough.
More information: https://lacoco-lab.github.io/courses/brain-2025/
Places: 12
Reproducible methods in metagenomics by Johanna Schmitz, Jens Zentgraf and Sven Rahmann
In this seminar, we discuss recently published papers that introduced novel methods for metagenomic data analysis, mainly focusing on efficient algorithms and data structures for sequence data.
Starting with the original paper, participants shall explain the underlying method with all necessary background and apply the tool to check the reproducibility of the results in the paper.
To pass the seminar both a presentation (40 min for seminars and 30 min for pro-seminars) and a written report are required.
A kick-off meeting to discuss the organization and possible papers will be held early in the semester. Participation in this kick-off meeting is only possible if you have been assigned to this seminar by the seminar system. You will then receive an email with the exact date and time. To participate in the seminar, participation in the kick-off meeting is mandatory.
Possible topics include:
- data processing (quality control, contamination removal etc.)
- taxonomic classification
- (meta)genome assembly
- metagenomic phylogeny
Requirements: Basic knowledge about algorithms and data structures, e.g., Introduction to Algorithms and Data Structures, Bioinformatik I, or Algorithms for Sequence Analysis
Places: 15