Dr. Vikram Sunkara
Programmbereich 3, PB 3 – Systemrheumatologie
Gruppenleitung: Erklärbare KI für Entzündungen
Liaisonarbeitsgruppe mit Zuse-Institut Berlin (ZIB)
Scientific Background
2012-2014
Postdoc. Karlsruhe Institute of Technology
2014-2015
Postdoc. University of Adelaide
2015-2019
Postdoc. Zuse Institute Berlin
2019-2020
Postdoc Fu Berlin / Zuse Institute Berlin
since 2021
Group Leader, Zuse Institute Berlin
since 12/2023
Group Leader, “Artificial Intelligence for Inflammation at the DRFZ Berlin
2013
Doctor of Philosophy (Mathematics) Australian National University
2019-2021
Postdoctoral Representative on the Board of the Mathplus Centre of Excellence
2020-2021
Member of Gender and Diversity Committee
TOP PUBLICATIONS
Chimeric U-Net – Modifying the standard U-Net towards Explainability Schulze, K., Peppert, F., Schütte, C., & Sunkara, V. (2024).Chimeric U-Net – Modifying the standard U-Net towards Explainability, Artificial Intelligence 388, 104240. https://doi.org/10.1016/j.artint.2024.104240
On the Sufficient Condition for Solving the Gap-Filling Problem Using Deep Convolutional Neural Networks Peppert, F., Von Kleist, M., Schutte, C., & Sunkara, V. (2022). On the Sufficient Condition for Solving the Gap-Filling Problem Using Deep Convolutional Neural Networks. IEEE Trans Neural Netw Learn Syst,33(11), 6194-6205. https://doi.org/10.1109/TNNLS.2021.3072746
Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint Sunkara, V., Heinz, G. A., Heinrich, F. F., Durek, P., Mobasheri, A., Mashreghi, M. F., & Lang, A. (2021). Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint. Osteoarthritis Cartilage, 29(6), 905-914. https://doi.org/10.1016/j.joca.2021.03.007
Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments Raharinirina, N. A., Peppert, F., von Kleist, M., Schutte, C., & Sunkara, V. (2021). Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments. Patterns (N Y), 2(9), 100332. https://doi.org/10.1016/j.patter.2021.100332
How to calculate pH-dependent binding rates for receptor–ligand systems based on thermodynamic simulations with different binding motifs How to calculate pH-dependent binding rates for receptor-ligand systems based on thermodynamic simulations with different binding motifs, Molecular Simulation, 2020, https://doi.org/10.1080/08927022.2020.1839660
Single-molecule analysis reveals agonist-specific dimer formation of µ-opioid receptors Moller, J., Isbilir, A., Sungkaworn, T., Osberg, B., Karathanasis, C., Sunkara, V., Grushevskyi, E. O., Bock, A., Annibale, P., Heilemann, M., Schutte, C., & Lohse, M. J. (2020). Single-molecule analysis reveals agonist-specific dimer formation of micro-opioid receptors. Nat Chem Biol, 16(9), 946-954. https://doi.org/10.1038/s41589-020-0566-1
