Dr. Nicholas Schreck

Areas of Interest:

• Linear mixed models
• Simulation studies
• Observational & Biomarker Studies
• High-dimensional regression

Education:

• Dr. rer. nat., Mathematics, University of Mannheim, 2017-2018
• Master of Science, Mathematics in Business and Economics, 2014-2016
• Bachelor of Science, Mathematics in Business and Economics, 2011-2014

Previous appointments:

• Research assistant, Institute of Mathematics, University of Mannheim, 2017-2019

Selected publications:

• Schreck, N., Slynko, A., Saadati, M., Benner, A. (2023) Statistical Plasmode Simulations: Potentials, Challenges and Recommendations. arXiv preprint. https://arxiv.org/abs/2305.06028
• Rädsch, T., Reinke, A., Weru, V., Tizabi, M. D., Schreck, N., et. al., (2023) Labelling instructions matter in biomedical analysis. Nature Machine Intelligence 5: 273-283
• Ayala, L., Adler, T. J., Seidlitz, S., [...], Schreck, N., et. al., (2023) Spectral imaging enables contrast agent–free real-time ischemia monitoring in laparoscopic surgery. Sci.Adv.9, eadd6778(2023). doi:10.1126/sciadv.add6778
• Schreck, N. and Wiesenfarth, M., (2022) Decomposition of the Explained Variation in the Linear Mixed Model. Arxiv Preprint
• Korell, F., Schreck, N., Müller-Tidow, C., et. al., Pre-transplant EASIX and sepsis after allogeneic stem cell transplantation. Intensive Care Med 48, 753–755 (2022). https://doi.org/10.1007/s00134-022-06676-3
• Pabst, C., Schreck, N., Benner, A., et al., Statin-based endothelial prophylaxis and outcome after allogeneic stem cell transplantation. Eur J Clin Invest. 2022; 00:e13883. doi: 10.1111/eci.13883
• Korell, F., Penack, O., Mattie, M., Schreck, N., et. al. (2022). EASIX and severe endothelial complications after CD19-directed CAR-T cell therapy–a cohort study. Frontiers in immunology 13:877477. https://doi.org/10.3389/fimmu.2022.877477
• Schellenberg M., Dreher, K. K., Holzwarth, N., [...], Schreck, N., et. al., (2022) Semantic segmentation of multispectral photoacoustic images using deep learning.
• Photoacoustics 26: 100341, ISSN 2213-5979, https://doi.org/10.1016/j.pacs.2022.100341.
• Studier-Fischer, A., Seidlitz, S., Sellner, J., [...], Schreck, N., et. al., (2022) Spectral organ fngerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model. Sci Rep 12: 11028, https://doi.org/10.1038/s41598-022-15040-w
• Basset, M., Kimmich, C.R., Schreck, N., et. al., (2021) Lenalidomide and dexamethasone in relapsed/refractory immunoglobulin light chain (AL) amyloidosis: results from a large cohort of patients with long follow-up. Br J Haematol 195: 230-243.https://doi.org/10.1111/bjh.17685
• Schreck, N., Piepho, H.-P., Schlather, M., (2019) Best Prediction of the Additive Genomic Variance in Random-Effects Models, Genetics 213: 379-394, https://doi.org/10.1534/genetics.119.302324

Other:

• Interview GIT: https://analyticalscience.wiley.com/do/10.1002/gitfach.19059/full/