Stefan Neumann, MSc

 

Faculty of Computer Science
University of Vienna
Room 6.23
Waehringerstrasse 29/6.32
1090 Wien-Vienna, Austria

email: stefan.neumann@univie.ac.at 
phone +43-1-4277-78341
web: personal homepage

Teaching: course directory

 

Supervisor:
Monika Henzinger

MSc (Computer Science)

Research area in the program
Combinatorial Optimization

Keywords

  • dynamic graph algorithms
  • conditional lower bounds
  • cell-probe lower bounds
  • data mining

Research Interests

My research interests are centered around the analysis of dynamic graph algorithms. For these problems I am particularly interested in deriving lower bounds (both conditional and unconditional) and upper bounds (fully dynamic algorithms and dynamic algorithms with only a small number of updates). Besides that, I also like data mining.

Scientific CV

Since 2016, PhD student at University of Vienna
2013-2015, MSc in Computer Science at Max Planck Institute of Computer Science and Saarland University
2010-2013, BSc in Mathematics at University of Jena

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Publications

Showing entries 0 - 10 out of 14

Neumann, S & Miettinen, P 2020, Biclustering and Boolean Matrix Factorization in Data Streams. in 46th International Conference on Very Large Data Bases (VLDB) 2020. 46th International Conference on Very Large Data Bases (VLDB) 2020, Tokio, Japan, 31/08/20.


Henzinger, M, Neumann, S & Wiese, A 2020, Dynamic Approximate Maximum Independent Set of Intervals, Hypercubes and Hyperrectangles. in 36th International Symposium on Computational Geometry (SoCG 2020). 36th International Symposium on Computational Geometry (SoCG 2020), Zürich, Switzerland, 22/06/20. doi.org/10.4230/LIPIcs.SoCG.2020.51


Miettinen, P & Neumann, S 2020, Recent Developments in Boolean Matrix Factorization. in 29th International Joint Conference on Artificial Intelligence (IJCAI) 2020. 29th International Joint Conference on Artificial Intelligence (IJCAI) 2020, Yokohama, Japan, 11/07/20.


Henzinger, M, Neumann, S & Schmid, S 2019, Efficient Distributed Workload (Re-)Embedding. in ACM SIGMETRICS / IFIP Performance 2019: Phoenix, AZ, USA — June 24 - 28, 2019. pp. 43-44, ACM SIGMETRICS / IFIP Performance 2019, Arizona, United States, 24/07/19. doi.org/10.1145/3309697.3331503


Neumann, S 2019, Finding Tiny Clusters in Bipartite Graphs. in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik - Informatik für Gesellschaft. doi.org/10.18420/inf2019_30


Neumann, S 2018, Bipartite Stochastic Block Models with Tiny Clusters. in Thirty-second Conference on Neural Information Processing Systems (NIPS). The Thirty-second Annual Conference on Neural Information Processing Systems, Montréal, Canada, 3/12/18.


Neumann, S, Ritter, J & Budhathoki, K 2018, Ranking the Teams in European Football Leagues With Agony. in 5th Workshop on Machine Learning and Data Mining for Sports Analytics at ECML/PKDD 2018. 5th Workshop on Machine Learning and Data Mining for Sports Analytics at ECML/PKDD 2018, Dublin, Ireland, 10/09/18. doi.org/10.1007/978-3-030-17274-9_5


Henzinger, M, Lincoln, A, Neumann, S & Vassilevska Williams, V 2017, Conditional Hardness for Sensitivity Problems. in CH Papadimitrou (ed.), 8th Innovations in Theoretical Computer Science Conference (ITCS 2017)., 26. doi.org/10.4230/LIPIcs.ITCS.2017.26


Showing entries 0 - 10 out of 14