Student projects
How to apply
We continuously supervise BSc/MSc theses, BSc/MSc Independent Studies, and MSc Master's Projects.The list below contains available projects and a reference contact person for each. To apply for a listed project, please e-mail the contact person with (a) your motivation for the project, (b) your CV and transcript of records, and (c) time constraints.
If you have a specific topic idea that is not listed, try to find the closest match with either one of the available projects or the interests of one of our group's members. You can then reach out to the respective group member. When doing so, please include a description of your project idea (or a project proposal) in addition to (a) your motivation for the project, (b) your CV and transcript of records, and (c) time constraints.
List of Available Topics
| Topic | Keywords | Reference Person |
|---|---|---|
| Parasocial relationships with AI: between companionship and fan fiction — Examining stylistic and emotional parallels between AI companion interactions and fan fiction communities, with a focus on risks for young users | web scraping, computational text analysis | Aleksandra Urman Prof. Dr. Anikó Hannák |
| Advertising in generative AI tools/chatbots — presence, design, and influence — How are commercial interests embedded in GenAI interfaces, and do they shape outputs or recommendations? | web scraping, computational text analysis | Aleksandra Urman Prof. Dr. Anikó Hannák |
| Does the open-source government software work as intented? — A hands-on functional audit of public government GitHub repositories, assessing whether tools perform as intended and where they fail/introduce bias | web scraping, systematic prompting experiments, comparative output analysis | Aleksandra Urman Prof. Dr. Anikó Hannák |
| Does AI-powered literature search reproduce scientific consensus? — Auditing tools like Google Scholar AI by comparing their recommendations against canonical reading lists across research domains | systematic tool auditing, web scraping, and bibliometric analysis | Aleksandra Urman Prof. Dr. Anikó Hannák |
| Did generative AI make bad citations worse? — Tracking the prevalence and spread of low-quality or hallucinated references in academic literature before and after 2022 | bibliometric, web scraping, network science methods | Aleksandra Urman Prof. Dr. Anikó Hannák |
| How much of Spotify is fake? — Auditing the presence of artificial artists and bot-driven streams, and how they embed themselves in curated playlist ecosystems. | web scraping, data analysis | Aleksandra Urman Prof. Dr. Anikó Hannák |
| Do the same models behave differently when accessed via API, consumer app, or third-party wrapper? | web scraping, systematic prompting experiments, comparative output analysis | Aleksandra Urman Prof. Dr. Anikó Hannák |
|
Evaluating biases in generative AI (ChatGPT/Bard/Bing Chat/Midjourney/DALL E 3) EARLIEST STARTING DATE APRIL 2025 |
web scraping, work with APIs, basic statistics | Aleksandra Urman |
| Literature review on Social Machine Behavior | data-analysis, interest in Machine Behavior, LLMs, AI agents | Nicolò Pagan |
|
LLM Cover Letter generation and evaluation: a comparative study |
LLM, basic statistics | Nicolò Pagan |
|
Exploratory analysis of Moltbook |
web scraping, basic statistics | Nicolò Pagan |
|
Dynamics of consensus in LLMs opinion formation |
LLM, basic statistics | Nicolò Pagan |
| Classifying Academic Research Fields from ORCID Dataset | Big Data, Machine Learning, Science of Science | Prof. Dr. Anikó Hannák |
| Creating a unique and comprehensive chess player and record data base (from e.g., the FIDE website, chessbase, and public profiles on online platforms, like lichess and chess.com) | Data scraping, linking data sets, big data | Prof. Dr. Anikó Hannák Nicolò Pagan |
| Counterfactual explanations and algorithmic recourse in stable matching or for non-linear utility functions |
Explainable AI, theoretical work |
Meirav Segal |
| Strategic manipulation of users via algorithmic recourse alterations and its effect on system utility | Coding simulations (Python), statistical analysis | Meirav Segal |
| Exploring feedback loops in ride-sharing systems and their impact on fairness | Coding simulations (Python), statistical analysis, algorithmic fairness | Meirav Segal |
| Training classifiers that take algorithmic recourse into account | Machine learning, model training, theoretical work, explainable AI | Meirav Segal |
| Effect of multiple recommender systems on a single platform on the contents visibility. | Agent-based modelling, basic statistic analysis, simulations on Python | Salima Jaoua |
More projects will be posted soon!