I am a Research Associate at the department of Computer Science at Boston University and at the Chair for Statistics and Data Science in Social Sciences and the Humanities at the Ludwig Maximilian University of Munich.

My research focuses on quantitative methodology, where I am specifically interested in the application of deep learning algorithms to social science problems (e.g., multiple imputation of missing data and synthetic data for data sharing). Substantively, I am interested in the prediction of political behavior and the ethical implications of new trends in applied social science research like Big Data and Artificial Intelligence with a focus on privacy. I have published multiple papers in internationally renowned journals and through invited talks I got the chance to communicate the results of my research to international audiences.

Furthermore, I am co-founder, contributor and the visualizationist of – a website that communicates a scientific forecast for German Federal elections to a broad audience.


  • Machine Learning
  • Deep Learning
  • (Differential) Privacy
  • Big Data
  • Data Visualization
  • Voting Behavior
  • (Field-) Experimental Research


  • Ph.D., 2023

    Graduate School of Economic and Social Sciences, University of Mannheim

  • M.A. in Political Science, 2016

    University of Mannheim

  • B.A. in Governance and Public Policy, 2013

    University of Passau

Publications & Work in Progress

Private Post-GAN Boosting

Differentially private GANs have proven to be a promising approach for generating realistic synthetic data without compromising the …

Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data

Recent advances in generating synthetic data that allow to add principled ways of protecting privacy – such as Differential …

An Approach to Predicting the District Vote Shares in German Federal Elections

Almost half of the total seats in the German Bundestag are awarded through first-past-the post elections at the electoral-district …

Forecasting Elections in Multi-Party Systems: A Bayesian Approach Combining Polls and Fundamentals

We offer a dynamic Bayesian forecasting model for multi-party elections. It com- bines data from published pre-election public opinion …

How Cross-Validation Can Go Wrong and What to Do About it.

The introduction of new “machine learning” methods and terminology to political science complicates the interpretation of …


I am a teaching instructor for the following courses at the University of Mannheim:

In 2020 I was a lecturer for at the University of California, Berkeley:

I also taught at the University of Applied Sciences Ludwigshafen:

Besides that, I am also a instructor of professional training workshops:


Best course this semester, thank you! (University of Mannheim)

Marcel was extremely good. Kept everyone engaged, curious and alert. I don’t think there was even a single question that he could not answer correctly. He was available all the time, on slack, mail, piazza. (University of California, Berkeley)

Wonderful! This tutorial and it’s corresponding course were my favorite. Marcel is a great teacher, a great speaker, and creates a great classroom environment. He is very supportive and encouraging. I always enjoyed attending and wish there were future tutorials and courses to attend. (University of Mannheim)

This is hands down the best course till now, both Daniel and Marcel are excellent teachers and effectively breaks down the concept in understandable concepts easy to consume. (University of California, Berkeley)

Marcel is an excellent tutor who knows his stuff very well an animates us students to further engage with quantitative methods. Great Job! (University of Mannheim)

Marcel was one of the best tutors I had in my 5 years at German universities. He was very helpful, open for questions, friendly towards students and easy to approach. (University of Mannheim)

Excellent course. I felt myself getting more and more employable from one session to the next. Really cool stuff we learn! (University of Mannheim)