{"id":20029,"date":"2026-02-14T13:58:00","date_gmt":"2026-02-14T12:58:00","guid":{"rendered":"https:\/\/wi.bwl.uni-mainz.de\/?page_id=20029"},"modified":"2026-04-29T14:13:23","modified_gmt":"2026-04-29T12:13:23","slug":"clarissa-kraemer","status":"publish","type":"page","link":"https:\/\/wi.bwl.uni-mainz.de\/en\/clarissa-kraemer\/","title":{"rendered":"Clarissa Kr\u00e4mer"},"content":{"rendered":"<\/jgu-base-heading>\n\n
\n
\n \n<\/jgu-base-image>\n\n\n \n\n \n<\/jgu-base-listitem>\n\n\n \n<\/jgu-base-listitem>\n\n\n \n<\/jgu-base-listitem>\n\n<\/jgu-base-contactcard>\n\n\n

<\/p>\n<\/div>\n\n\n\n

<\/div>\n\n\n\n
\n \n\n \n\n

Clarissa Kr\u00e4mer (M.Sc.) is currently a doctoral candidate at the chair and conducts research on machine learning for epidemiological data and anomaly detection. Since 2026, she has been a Junior Fellow of the Gutenberg Academy Fellows Program and has been employed as an academic staff member at the chair of Prof. Dr. Rothlauf since 2024. Previously, she studied Digital Business Management (B.Sc.) from 2018 to 2021 at the RheinMain higher education institution in Wiesbaden and Management (M.Sc.) from 2021 to 2024 at the Johannes Gutenberg University Mainz. During her program of study at the Johannes Gutenberg University Mainz, she also spent 4 months as an Erasmus scholar at the SGH Warsaw School of Economics in Poland. <\/p>\n\n\n\n

<\/p>\n\n\n<\/jgu-base-tabsitem>\n\n\n \n\n

My research interests include the application of approaches from machine learning<\/strong>. In particular, I focus on the use of machine learning methods for epidemiological data. <\/p>\n\n\n<\/jgu-base-tabsitem>\n\n\n \n\n \n<\/jgu-base-buttonitem>\n\n<\/jgu-base-button>\n\n\n

<\/p>\n\n\n\n\n

2026<\/h3>\n\t
\n\n\t\n\t
\n
Kr\u00e4mer, C., Schwarz, L., and Rothlauf, F. (2026). Anonymization of Electronic Health Records by the Use of Dimensionality Reduction Techniques. In Studies in Health Technology and Informatics<\/i>. IOS Press. DOI<\/a><\/div>\n
R\u00f6chner, P., Kr\u00e4mer, C., Mayer, J. U., et al. (2026). An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing<\/i>. Author\/Publisher URL<\/a><\/div>\n<\/div>\t\n<\/div>\n\n

2025<\/h3>\n\t
\n\n\t\n\t
\n
Albrecht, S., Kr\u00e4mer, C., R\u00f6chner, P., et al. (2025). Integrating the ENCODE blocklist for machine learning quality control of ChIP-seq data with seqQscorer. In openRxiv<\/i>. DOI<\/a><\/div>\n
Kr\u00e4mer, C., Schmitt, S., Rothlauf, F. (2025). Using Machine Learning for the Fusion of Tumor Records on a Real-World Dataset. In Studies in Health Technology and Informatics<\/i>. IOS Press. DOI<\/a><\/div>\n<\/div>\t\n<\/div>\n\n\n<\/jgu-base-tabsitem>\n\n\n \n\n

I am currently supervising the following subjects:<\/p>\n\n\n\n