Student's Corner

Ahmed Hossameldin Mohamed summarizes his Master’s thesis, where he explored how multi-agent AI systems can transform sparse biomedical knowledge graphs into dense, validated, and traceable resources for scientific discovery.

Atefeh Habibi presents her thesis on an AI-powered pipeline that unifies clinical trial and literature data for aging research. By replacing keyword searches with smart concept mapping, her work builds a massive, interconnected knowledge graph that dramatically improves how scientists find and connect biomedical evidence.

Baris Yapici summarizes his Master's thesis in which he used Knowledge Graphs to identify promising data collaborations between researchers and research projects.

PhD student Jannis Guski presents his latest paper in which he applies causal machine learning techniques to analyze whether real-world patient data hint at an increased risk of neurodegeneration caused by SARS-CoV-2 infections. The work was conducted as part of the EU project COMMUTE.

Lennart Carstens-Behrens shares some insights into his work on the estimation of causal effects that lifestyle factors might have on the risk of Parkinson's disease.

India Mähler presents her master's thesis, which she recently defended as part of the applied mathematics degree program at Koblenz University of Applied Sciences. Her thesis was supervised by Prof. Dr. Holger Fröhlich and Prof. Dr. Armin Fiedler.

Jonas Elmer describes how he used generative adversarial networks to generate synthetic data for minorities in clinical cohorts in his recently submitted Bachelor's thesis.

Marjan Niazpoor explains how suitable bioassays for the validation of structured biomedical hypotheses can be identified with the aid of Knowledge Graphs and Large Language Models, the topic of her recently submitted master's thesis.

Leonie Fohler presents her master's thesis, which she recently defended as part of the applied mathematics degree program at Koblenz University of Applied Sciences. Her thesis was supervised by Prof. Dr. Holger Fröhlich and Prof. Dr. Michael Kinder.

Master's student Johanna Driever talks about her latest paper in which she used Domain Adaptation to generalize Machine Learning predictions across EHR datasets from different healthcare systems.