Marco Schäfer, M.Sc.

Ph.D. Student, Big Data Visual Analytics in Life Sciences

Office
Sand 14
72076 Tübingen
Room C105 (ground floor)
 +49 7071 29-77047

marco.schaeferspam prevention@uni-tuebingen.de


Research Projects

PROLINT – Visual Analysis of Protein-Ligand Interactions (see projects website).

 


Timeline

2018-today
research assistant and doctoral student

in the group of Jun.-Prof. Dr. Michael Krone - Big Data Visual Analytics in Life Sciences - University of Tübingen

2015-2018
Master Biosystemtechnique/Bioinformatic

at TH Wildau - Technical University of Applied Science

2012-2015
Bachelor Biosystemtechnique/Bioinformatic

at TH Wildau - Technical University of Applied Science

2012
Abitur - General University Entrance Qualification

1992
born in Marburg


Publications

2019 - 2021

2021
  • K. Schatz, J. J. Franco-Moreno, M. Schäfer, A. S. Rose, V. Ferrario, J. Pleiss, P.-P. Vázquez, T. Ertl, and M. Krone, “Visual Analysis of Large-Scale Protein-Ligand Interaction Data,” Computer Graphics Forum, 2021. https://doi.org/10.1111/cgf.14386
  • K. Schatz, F. Frieß, M. Schäfer, P. C. F. Buchholz, J. Pleiss, T. Ertl, and M. Krone, “Analyzing the similarity of protein domains by clustering Molecular Surface Maps,” Computers & Graphics, 2021. https://doi.org/10.1016/j.cag.2021.06.007
  • P. Hermosilla, M. Schäfer, M. Lang, G. Fackelmann, P. Vázquez, B. Kozlíková, M. Krone, T. Ritschel, T. Ropinski, “Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures,” in International Conference on Learning Representations (ICLR), 2021. https://doi.org/10.48550/arXiv.2007.06252
2020
  • K. Schatz, F. Frieß, M. Schäfer, M. Krone, T. Ertl, “Analyzing Protein Similarity by Clustering Molecular Surface Maps,” in EG Workshop on Visual Computing for Biology and Medicine, 2020 . https://doi.org/10.2312/vcbm.20201177
2019
  • M. Schäfer, M. Krone, “A Massively Parallel CUDA Algorithm to Compute and Visualize the Solvent Excluded Surface for Dynamic Molecular Data,” in EG/EuroVis 2019 Workshop on Molecular Graphics and Visual Analysis of Molecular Data (MolVA), 2019. https://doi.org/10.2312/molva.20191094