A description of my research project can be found on my page of the research school of Data Science and Engineering website. 

> Interpretability for genomic convolutional neural networks

> Trustworthy machine learning

> Deployment of AI for medicine and healthcare

Winter term 2022/2023

• Master seminar: From fairness to cyberbiosecurity: accountability in machine learning for biology and medicine

Summer term 2022

• Master seminar: Mishaps in Statistics and ML

Winter term 2021/2022

• Master seminar Verantwortung in der Informatik - Accountability in AI
• Schülerkolleg ('Introduction to programming', 7^th-8^th grade)

• Poster presentation at ECCB 2022: "Influence of motif interactions on post-hoc attribution methods in genomic CNN's"
• Talk at the Joint Workshop of the German Research Training Groups in Computer Science in Dagstuhl 2022: "Interpretability of motif interactions in genomic convolutional neural networks"
• Poster Presentation at ISMB conference 2021: "Evaluation Of Convolutional Neural Networks Containing Interactions Between Genomic Motifs" (MLCSB & Representational Learning in Biology sessions)

Hauschild, A. C., Lemanczyk, M., Matschinske, J., Frisch, T., Zolotareva, O., Holzinger, A., ... & Heider, D. (2022). Federated Random Forests can improve local performance of predictive models for various healthcare applications. Bioinformatics, 38(8), 2278-2286.  

https://doi.org/10.1093/bioinformatics/btac065