Seminar Assignment Winter 2025

The central registration for all computer science seminars will open on Sep 5th.

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 Oct 14th, 23:59 CET. You can select which seminar you would like to take, and will then be automatically assigned to one of them on Oct 17th.

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, please do not 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"). The system will then prioritize you for assigning 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 Oct 16th, 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

Aspects of Quantitative Program Verification by Benjamin Kaminski, Tobias Gürtler, Anran Wang

Are you passionate about logic, verification, semantics, and alike? Are you tired of thinking black-and-white, true-or-false? Then come and study the more nuanced quantitative formal program verification! In quantitative verification, properties are not just true or false. Instead, we verify quantities like runtimes, error probabilities, beliefs, etc.

Topics which we will cover include:

- Probabilistic programming (a currently trending modeling paradigm in machine learning),

- The geometry of neural networks

- Incorrectness logic (the latest creation of the former chief formal methods researcher at Facebook)

- The flow of quantitative information through programs

- Worst-case execution times

- Verification of heap-manipulating programs

- and many more

The seminar website can be found here:

https://quave.cs.uni-saarland.de/teaching/teaching-ws-2024-2025/aspects-of-quantitative-program-verification-ws-24-25/

Requirements: The most important requirement: You should really really like math and/or logic. This seminar covers very theoretical work.

The following courses are mandatory and/or recommended.

Mandatory: Programmierung 1, Programmierung 2, Grundzüge der Theoretischen Informatik

Recommended: Semantics; Introduction to Computational Logic; Automata, Games and Verification;

Ideal: Verification

Places: 6

Seminar Legal Tech und eJustice by Christoph Sorge

Das Seminar „Legal Tech und eJustice” ist ein interdisziplinäres Seminar für Informatiker und Juristen.

Inhalte des Seminars:
Der Begriff "eJustice" bezeichnet die Digitalisierung im Justizwesen, mithin den Einsatz von IT-Verfahren bei Gericht und Anwaltschaft. Dies umfasst beispielsweise die elektronische Aktenführung, Online-Gerichtsverfahren und digitale Kommunikation zwischen Gerichten, Anwälten und Behörden, um gerichtliche Prozesse effizienter, transparenter und zugänglicher zu gestalten.

"Legal Tech" bezeichnet die Informationstechnik zur Unterstützung juristischer Arbeit, wie beispielsweise automatisierte Vertragsprüfung, KI-gestützte Rechtsberatung, digitale Tools zur Dokumentenerstellung.

Ablauf des Seminars:
Vorbesprechung: Einführung und Themenvergabe.
Abstracts und Preprints: Einreichung und Feedback-Runden.
Peer Reviews: Bewertung der Arbeiten durch andere Teilnehmer.
Vorträge: Präsentation der Seminararbeiten.
Abschlussarbeit: Finale Abgabe der ausgearbeiteten Themen

Requirements: Es wird erwartet, dass die Teilnehmer in der Lage sind, Vorträgen in deutscher Sprache zu folgen und Ausarbeitungen in deutscher Sprache im Rahmen des Peer Reviews zu lesen (eigene Vorträge und Ausarbeitungen können aber in deutscher oder englischer Sprache angefertigt werden).

Places: 7

Theoretical Abilities and Limitations of Language Models by Xinting Huang, Michael Hahn

LLM have amazing capabilities, but also hallucinate and make reasoning mistakes. Can we understand these abilities and limitations theoretically, as way to figure out ways of overcoming them? The incredible scale of current LLMs makes this a daunting prospect. However, recent research has developed mathematical understanding sheeding light on key questions, such as: why LLMs struggle with basic tasks like arithmetic, what kinds of problems transformers can and cannot solve without chain-of-thought, how they generalize, and what architectures might come next. Drawing on ideas from complexity theory, formal languages, and learning theory, the seminar is open to all with a willingness to engage deeply with technical content.

https://lacoco-lab.github.io/courses/theory-25/

Requirements: Good understanding of Machine Learning and Neural Networks. Good foundation in Math and Theoretical Computer Science

Places: 12