Bastian Prasse

In May 2021, Bastian Prasse obtained his PhD cum laude, the highest distinction at Delft University of Technology, under the supervision of Professor Piet Van Mieghem. As a graduate of the double degree programme Top Industrial Managers for Europe, he obtained an M.Sc. degree in Systems and Control Theory at the Royal Institute of Technology, Sweden, and an M.Sc. degree in Computer Engineering (Dean’s List, awarded to the top 5% of graduates) at RWTH Aachen University, Germany, both in 2015. In 2012, he obtained a B.Sc. degree in Computer Engineering (Dean’s List) at RWTH Aachen University. He was a recipient of the Texas Instruments and ICE Bachelor Thesis Award and was awarded the German National Scholarship and the North Rhine-Westphalia Scholarship.

His main research interests lie in the analysis, prediction and network reconstruction for dynamics on networks. In particular, his work focusses on epidemics on networks, and the relation of structure and function in brain networks. Also see the Google scholar profile.

Below are two presentations of his work:

              

 

Publications and Reports

 

  1. Interlayer Connectivity Reconstruction for Multilayer Brain Networks using Phase Oscillator models, P. Tewarie, B. Prasse, J. Meier, A. Byrne, M. Domenico, C. J. Stam, M. Brookes, A. Hillebrand, A. Daffertshofer, S. Coombes, Stephen and P. Van Mieghem, New Journal of Physics, to appear.
  2. Clustering for epidemics on networks: A geometric approach, B. Prasse, K. Devriendt and P. Van Mieghem, Chaos: An Interdisciplinary Journal of Nonlinear Science, to appear.
  3. Network-based Prediction of COVID-19 Epidemic Spreading in Italy,  C. Pizzuti, A. Socievole, B. Prasse, P. Van Mieghem, Applied Network Science, November 2020.
  4. Comparing the Accuracy of Several Network-based COVID-19 Prediction Algorithms,  M. A. Achterberg, B. Prasse, L. Ma, S. Trajanovski, M. Kitsak, P. Van Mieghem, International Journal of Forecasting, October 2020.
  5. Time-Dependent Solution of the NIMFA Equations around the Epidemic ThresholdB. Prasse and P. Van Mieghem, Journal of Mathematical Biology, September 2020.
  6. Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province HubeiB. Prasse, M. A. Achterberg, L. Ma and P. Van Mieghem, Applied Network Science, July 2020.
  7. Mobile smartphone tracing can detect almost all SARS-CoV-2 infectionsB. Prasse and P. Van Mieghem, arXiv preprint arXiv:2006.14285, June 2020.
  8. Predicting Dynamics on Networks Hardly Depends on the TopologyB. Prasse and P. Van Mieghem, arXiv preprint arXiv:2005.14575, May 2020.
  9. Fundamental Limits of Predicting Epidemic OutbreaksB. Prasse, M. A. Achterberg and P. Van Mieghem, TU Delft report, 2020.
  10. Network Reconstruction and Prediction of Epidemic Outbreaks for General Group-Based Compartmental Epidemic ModelsB. Prasse and P. Van Mieghem, IEEE Transactions on Network Science and Engineering, to appear.
  11. Mapping functional brain networks from the structural connectome: relating the series expansion and eigenmode approaches, P. Tewarie*, B. Prasse*, J. M. Meier, F. A. N. Santos, L. Douw, M. Schoonheim, C. J. Stam, P. Van Mieghem, A. Hillebrand, NeuroImage, April 2020. *P. Tewarie and B. Prasse contributed equally.
  12. The Viral State Dynamics of the Discrete-Time NIMFA Epidemic ModelB. Prasse and P. Van Mieghem, IEEE Transactions on Network Science and Engineering, 2019.
  13. Network Reconstruction and Prediction of Epidemic Outbreaks for NIMFA ProcessesB. Prasse and P. Van Mieghem, arXiv preprint arXiv:1811.06741, November 2018.
  14. Exact Network Reconstruction from Complete SIS Nodal State Infection Information Seems Infeasible, B. Prasse and P. Van Mieghem, IEEE Transactions on Network Science and Engineering, 2018.
  15. Maximum-Likelihood Network Reconstruction for SIS Processes is NP-HardB. Prasse and P. Van Mieghem, arXiv preprint arXiv:1807.08630, July 2018.
  16. Backtracking-based dynamic programming for resolving transmit ambiguities in WSN localization, S. Schlupkothen*, B. Prasse* and G. Ascheid*, EURASIP Journal on Advances in Signal Processing 2018.1 (2018): 20. *S. Schlupkothen, B. Prasse and G. Ascheid contributed equally.
  17. A Dynamic Pogramming Algorithm for Resolving Transmit-Ambiguities in the Localization of WSN, S. Schlupkothen, B. Prasse and G. Ascheid, Ad Hoc Networking Workshop (Med-Hoc-Net), 2016 Mediterranean. IEEE, 2016.

 

Presentations

 

  1. Prediction of epidemics on networks, Seminar of the Leiden Complex Networks Network (LCN2), Leiden, The Netherlands, 27 November 2020.
  2. Prediction of Epidemic Outbreaks for General Group-Based Epidemic Models, regular talk at NetSci 2020: International School and Conference on Network Science, September 2020.
  3. The Solution of the NIMFA Epidemic Model around the Epidemic Threshold, invited session at 21st IFAC World Congress, Berlin, Germany, 12-17 July 2020.
  4. TBA, Networks Seminar of the Mathematical Institute, University of Oxford, UK, 9 June 2020.
  5. Network Reconstruction from NIMFA Viral State Observations of Multiple Epidemic Outbreaks, regular talk at "7th International Conference on Complex Networks and Their Applications", Cambridge, UK, December 2018.
  6. Network Reconstruction from Viral State Observations of SIS Models seems Infeasible, contributed talk at NetSci 2018: International School and Conference on Network Science, Paris, France, June 2018.

 

Reviewer

 

IEEE Transactions on Automatic Control, Annual Reviews in Control, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Control Systems Technology, Physical Review E, Nature Scientific Reports, IEEE Control Systems Letters, International Journal of Forecasting, European Control Conference (ECC)

 

Contact

 

E-Mail This email address is being protected from spambots. You need JavaScript enabled to view it.

Contact

Mekelweg 4, 9th Floor
2628 CD, Delft, The Netherlands
Tel : +31 (0)15 27 86111