Project Supervision

Supervision of Bachelor and Master Theses

I currently supervise Bachelor and Master theses only when they are written at my current institute, the Institute of Econometrics and Empirical Economic Research at the University of Hohenheim.

If your thesis is formally assigned to the institute and you are interested in topics in artificial intelligence, econometrics, finance, empirical economics, or data science, please contact me with a short outline of your idea.

For final theses, please take a look at the rules about examining persons, working times, and other stipulations by the University of Hohenheim. Supervision may be conducted in cooperation with professors at the institute, depending on the topic and examination rules.

I may also supervise or advise applied seminar projects, semester projects, or AIDAHO cooperation modules when the topic fits my expertise and the formal responsibility is clear. These projects are not a substitute for the institute requirement for Bachelor and Master theses.

Thesis and project work offer a chance to explore topics that align with your academic interests while benefiting from structured guidance and feedback throughout the process. Whether you are working on econometric modeling, AI-related topics, finance, or empirical data analysis, the project should lead to a clear research question, a transparent method, and a reproducible output.

Here is a short overview what to expect during my supervision in such a project.

What I expect from students

• A focused research question: Come with a concrete topic idea, a first research question, and a short explanation of why the question matters.
• Early scoping: Clarify the empirical object, available data, expected method, and feasible output before collecting large amounts of material or writing long text sections.
• Independent work: You are responsible for day-to-day progress, literature search, coding, writing, and documenting decisions.
• Regular updates: Send concise progress updates before meetings, including what changed, what is blocked, and what decision you need.
• Reproducibility: Keep data, code, notes, and draft outputs organized so that results can be traced from raw inputs to final tables, figures, or arguments.
• Transparent methods: Explain why you chose a model, dataset, transformation, or robustness check; do not treat software output as self-explanatory evidence.
• Timely drafts: Share partial drafts early enough for substantive feedback. A full thesis sent shortly before submission cannot receive meaningful supervision.
• Integration of feedback: Treat feedback as work to be processed, not as optional commentary. If you disagree, explain the reason and propose an alternative.
• Time management: Maintain a realistic timeline with milestones for proposal, data preparation, analysis, writing, revision, and submission.
• Completion of tasks: Agreed work packages should be completed carefully, documented clearly, and communicated before deadlines slip.

What I do not expect

• Working without guidance: You are not expected to work without any support.
• Comprehensive prior knowledge: No extensive subject-specific expertise is required beforehand.

What I offer

• Realistic tasks: Meaningful project assignments with manageable workloads.
• Regular discussions: Scheduled meetings to discuss progress and challenges.
• Guidance: Advice on questions or problems brought to me.
• Text review: Review of texts or sections, providing actionable feedback for refinement.
• Methodological support: Assistance in choosing and applying suitable methods.

What I do not offer

• Proofreading: Detailed spelling or grammar corrections.
• Ready-made solutions: Fully developed answers to problems.
• Deadline management: Tracking your deadlines or maintaining a calendar for you.

Exemplary Project Timeline

Below is an example of a Bachelor thesis project timeline, which can also be adapted for Master theses by extending the overall timeframe.

While every project is unique, it is essential to begin with a proposal phase — often through a brief meeting or Zoom call — where you present your initial ideas. Together, we will determine a realistic path forward, including how to collect and process the necessary data, refine the methodology, and finalize the research question. By the end of this initial discussion, we should all have a clear understanding of the project’s scope and direction. If that clarity is missing, it may be best to reconsider or discontinue the project early to prevent future complications.

Please note that the timeline below is only an example. Dates, durations, and milestones should be adjusted to the formal requirements and scope of your project.