Short CV (long version available upon request).

Yan Scholten

I am a PhD student in the Data Analytics and Machine Learning group at the Technical University of Munich, supervised by Prof. Stephan Günnemann. My research centers on trustworthy AI, with a focus on developing methods that make machine learning more safe, reliable, and aligned with human values. I tackle core challenges in AI, such as machine unlearning, alignment, adversarial robustness, robustness certification, and conformal prediction. My recent work advances capabilities and reliability of large language models (LLMs).

Education

  • 2022 - now: PhD candidate in Computer Science, Technical University of Munich
  • 2019 - 2022: M.Sc. Informatics, Technical University of Munich (with high distinction)
  • 2015 - 2019: B.Sc. Computer Science (Math Minor), Paderborn University (with distinction)

Research Experience

  • Sep 2022 - today: PhD student, Technical University of Munich
  • May 2025 - Jul 2025: Research visit, Carnegie Mellon University, USA
  • May 2018 - Jul 2018: Undergraduate research visit, University of Western Ontario, Canada
  • Oct 2017 - Apr 2018: Undergraduate research assistant, Paderborn University, Germany

Academic Honors and Awards

  • 2025: Selected as Top Reviewer at NeurIPS 2025
  • 2023: Admission to the Konrad Zuse School of Excellence in Reliable AI
  • 2019: Deutschlandstipendium awarded by the Technical University of Munich
  • 2018: RISE worldwide scholarship awarded by DAAD
  • 2018: Deutschlandstipendium awarded by Studienfonds OWL
  • 2017: Admission to elite program of the EIM-faculty at Paderborn University

Theses

  • Master's thesis (2022): Interception Smoothing: Gray-box Certificates for Graph Neural Networks
  • Bachelor's thesis (2019): Towards a Large-Scale Causality Graph