Short Biography

• From 2013 - 2017 I did my Bachelor of Science in Business Mathematics at the University of Trier.
• From 2017 - 2020 I did my Master of Science in Business Mathematics at the Technical Unversity Berlin and worked as research student in the Machine Learning group at Fraunhofer HHI as well as at PwC DigiSpace in Berlin.
• In September 2020, I joined the Engineering Mathematics and Commputing Lab (EMCL) at IWR, University of Heidelberg. 
• Since December 2020, I am an associated researcher at HIDSS4Health.
   

Research Interests

• Mathematical Modeling in metabolic network
• Data Mining in Disease Diagnosis 
• Machine Learning and Deep Learning
• Explainable AI
• Newborn screening
   

Research activities

• Research stay in the Molecular systems physiology group at University of Galway, Ireland, headed by Prof. Dr. Ines Thiele from September - December 2022. 
• JIMD Podcast on "Machine learning in newborn screening", June 2022. 
   

Publications

2023

• E. Zaunseder, U. Mütze, S.F. Garbade, S. Haupt, P. Feyh, G.F. Hoffmann, V. Heuveline, S. Kölker, Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria. Metabolites 2023, 13, 304. https://doi.org/10.3390/metabo13020304.

2022

• Q. Tran, K. Shpileuskaya, E. Zaunseder, L. Putzar, S. Blankenburg: Comparing the Robustness of Classical and Deep Learning Techniques for Text Classification. 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1-10, doi: 10.1109/IJCNN55064.2022.9892242.
• E. Zaunseder, S. Haupt, U. Mütze, SF. Garbade, S. Kölker, V, Heuveline: Opportunities and Challenges in Machine Learning-based Newborn Screening - A systematic literature review. JIMD Reports, 2022. doi:10.1002/jmd2.12285.

2020

• L. Petry, H. Herold , G. Meinel, T. Meiers,  I. Müller, E. Kalusche, T. Erbertseder, H. Taubenböck, E. Zaunseder, V. Srinivasan, A. Osman, B. Weber, S. Jäger, C. Mayer, C. Gengenbach, AIR QUALITY MONITORING AND DATA MANAGEMENT IN GERMANY – STATUS QUO AND SUGGESTIONS FOR IMPROVEMENT; Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.; XLIV-4/W2-2020, 37–43; 2020; doi: https://doi.org/10.5194/isprs-archives-XLIV-4-W2-2020-37-2020

2018

• E. Zaunseder, L.Müller, S. Blankenburg: High Accuracy Forecasting with Limited Input Data: Using FFNNs to Predict Offshore Wind Power Generation; SoICT 2018: Proceedings of the Ninth International Symposium on Information and Communication Technology in Da Nang, Vietnam; doi: https://doi.org/10.1145/3287921.3287936