N-of-1 trials methodology, particularly the development of deep learning models for n-of-1 studies including suitable hypothesis tests; furthermore missingness and data imputation in n-of-1 trials, adaptive trials, causal inference, average effect estimation for aggregated n-of-1 trials.

Current Project(s):

• Generating synthetic image outcomes for n-of-1 trials
  • research seminar in WiSe 23/24

Past Project(s):

• Using imaging data as outcome measures in n-of-1 trials
  • research seminar in WiSe 22/23
• Improving image data embeddings in n-of-1 trials
  • research seminar in SoSe 23

• Research seminar
  • WiSe 23/24, topic: generating synthetic image outcome data for n-of-1 trials
  • SoSe 23, topic: improving image data embeddings in n-of-1 trials
  • WiSe 22/23, topic: development of classification models for imaging outcome data in n-of-1 trials
• Master projects
  • WiSe 23/24, topic: apative n-of-1 trials in StudyU
  • SoSe 23, topic: multimodal n-of-1 trials in StudyU
  • SoSe 22, topic: improvement of user journey in the StudyU designer app
• Master thesis supervision, topics:
  • blood glucose prediction and indices estimation in diabetes (2022/2023)
  • missingness in adaptive n-of-1 trials (2023/2024)
  • endometriosis prediction (2023/2024)
  • Supervised acne classification in n-of-1 trials (2023/2024)