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This pages lists the open BSc. and MSc. thesis descriptions, as well as the master projects opportunities currently available in the DDIS research group.
If you are interested in any of the listed projects, please do not hesitate to contact the person mentioned in the open topic description.
If there are currently no open topics but you are generally interested in our research (see https://www.ifi.uzh.ch/en/ddis/research.html), or if you would like to propose a thesis about your own idea, you can send us an email to ddis-theses@ifi.uzh.ch.
In political science, dimensionality reduction algorithms are often used to visualize the ideological orientation of voters or candidates in a one or two-dimensional space - referred to as ideal point estimation. Common choices are item-response theory or principal component analysis, for example in these applications: Smartmap or Voteview.
The goal of this master thesis is to develop a new ideal point estimation algorithm using a machine learning approach. The focus will be on support vector machines (SVM) since iteratively training SVMs has been shown to work well in initial experiments. Specifically, the low-dimensional coordinates should be optimized until the model best reconstructs the given data. Throughout the thesis, the implemented code should be parallelized and optimized, and results should be compared to the existing baselines using the political dataset of the Swiss Voting Advice Application Smartvote.
If interested, please get in touch with us at the email address below. We can provide a more detailed description during a meeting.
Requirements: Solid understanding of dimensionality reduction, support vector machines, and efficient programming in Python.
Start date: Open now.
Contact: Fynn Bachmann
With an overwhelming number of scientific articles available, effective interaction with them is important to facilitate the scientific text understanding. Users will experience a more intuitive way to skim through articles, with important elements like scientific hypotheses, claims, and evidence, instantly highlighted.
This master project focuses on developing a framework that enhances how users engage with scientific articles in HTML or PDF formats. It leverages our existing CrowdAlytics Annotation Tool, which supports the manual annotation and interaction with hypothesis, and aims to newly integrate advanced pre-trained large language models (LLMs) to automatically identify and highlight key scientific constructs such as hypotheses and/or arguments.
The project starts with the existing CrowdAlytics Ontology and SciHyp models (pre-trained LLMs), specifically trained for scientific hypotheses identification, and will expand to incorporate various annotation schemas and models as needed. For more information or to discuss this project in detail, please feel free to contact us at the provided email address.
Requirements 2-3 students
With programming skills
Preferably AI students who have knowledge about Semantic Web Technologies and LLMs
Expected Start date: As soon as possible.
Contact: Rosni Vasu
