Johannes Sorger joined the CSH as a postdoctoral researcher in Data Science and Visualization in October 2017. Johannes received his computer science degree in visual computing from the TU Wien at the Institute of Computergraphics and Algorithms where he also finished his PhD in a collaboration with the VRVis research center. Johannes’ main research interests are centred around the application of visualization as an enabling technology. For his work on the visualization of neuronal networks Johannes received the 2014 OCG Incentive Award, as well as the Best Paper Award at the 2013 IEEE Symposium on Biological Data Visualization.
During his time at the TU Wien, Johannes was involved in the development of visualization technology for the depiction of cellular data at atomic resolution that was awarded with the 2016 Austrian Computer Graphics Award for the best technical solution, as well as the 2017 Best Overall Concept Award in the i2c BootCamp for Science Preneurs. Besides neuroscience and cell science, Johannes’ research intersects a diverse range of scientific disciplines that also includes DNA technologies, as well as civil, and urban engineering.
Johannes has published and reviewed several works in top ranking visualization conferences and journals, such as the IEEE Vis, EuroVis, and Transactions on Visualization and Computer Graphics. He will contribute with his visualization experience to convey insights into the results and workings of the agent based simulations that are developed at the CSH Vienna.
J. Sorger, M. Waldner, W. Knecht, A. Arleo
Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation
In: 2nd International Conference on Artificial Intelligence & Virtual Reality (2019) 144–151
D. R. Lo Sardo, S. Thurner, J. Sorger, G. Duftschmid, G. Endel, P. Klimek
Quantification of the resilience of primary care networks by stress-testing the health-care system
PNAS 116 (48) (2019) 23930–23935
A. Arleo (...) J. Sorger et al.
Sabrina: Modeling and Visualization of Financial Data over Time with Incremental Domain Knowledge
In: 2019 IEEE Visualization Conference (VIS) (2019) 51–55