TU Wien Informatics

Overview Professors Scientific Staff Administrative Staff Student Staff External Lecturers By Responsibilities By Committee By Name
Related
Guest Professors Honoraries Alumni (LinkedIn) #5qw

Stefan Neumann

Assistant Prof. Dr.techn. / BSc MSc

Research Focus

  • Information Systems Engineering: 51%
  • Logic and Computation: 49%

Research Areas

  • Data Mining, Machine Learning, Graph Algorithms, social network analysis, Combinatorial Optimization, Theoretical Compurter Science, Algorithms and Data Structures
Stefan Neumann

About

My research focus is on algorithms for data science and for social network analysis. In particular, I am interested in the following topics:

Foundations of data science: I develop practical data science algorithms with provable guarantees. I am particularly interested in the beyond worst-case analysis of algorithms.

Social network analysis: I study how interventions, such as timeline algorithms, influence the polarization and the disagreement in (online) social networks.

I am also generally interested in graph algorithms and (dynamic) data structures.

For more information, please see my homepage https://neumannstefan.com/.

Roles

  • Assistant Professor
    Machine Learning, E194-06
  • Curriculum Coordinator
    Master / Area / Machine Learning

Contact

2025W

2026S

 

  • Discovering Opinion Intervals from Conflicts in Signed Graphs / Blohm, P., Chen, F., Gionis, A., & Neumann, S. (2025). Discovering Opinion Intervals from Conflicts in Signed Graphs. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. The Thirty-ninth Annual Conference on Neural Information Processing Systems, United States of America (the). http://hdl.handle.net/20.500.12708/223658
    Project: VRG-TOSN (2023–2031)
  • Optirefine: densest subgraphs and maximum cuts with k refinements / Tu, S., Stankovic, A., Neumann, S., & Gionis, A. (2025). Optirefine: densest subgraphs and maximum cuts with k refinements. Data Mining and Knowledge Discovery, 39(6), Article 82. https://doi.org/10.1007/s10618-025-01142-2
    Project: VRG-TOSN (2023–2031)
  • Calibrated and Diverse News Coverage / Zhou, T., Neumann, S., Garimella, K., & Gionis, A. (2025). Calibrated and Diverse News Coverage. In CIKM ’25: Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 4509–4518). Association for Computing Machinery. https://doi.org/10.1145/3746252.3761149
    Project: VRG-TOSN (2023–2031)
  • Mitigating Polarization and Disagreement Based on User Interests / Neumann, S. (2024, November 5). Mitigating Polarization and Disagreement Based on User Interests [Keynote Presentation]. MAMMOth Workshop — Part of an EU Horizon Project, Wien, Austria.
  • Sublinear-Time Opinion Estimation / Neumann, S. (2024, September 24). Sublinear-Time Opinion Estimation [Conference Presentation]. Dagstuhl Seminar 24391: Statistical and Probabilistic Methods in Algorithmic Data Analysis, Germany.
  • Sublinear-Time Clustering Oracle for Signed Graphs / Neumann, S. (2024, May 16). Sublinear-Time Clustering Oracle for Signed Graphs [Conference Presentation]. SIAM Linear Algebra 2024, Paris, France.
  • Sublinear-Time Opinion Estimation in the Friedkin--Johnsen Model / Neumann, S., Dong, Y., & Peng, P. (2024). Sublinear-Time Opinion Estimation in the Friedkin--Johnsen Model. In Proceedings of the ACM on Computer Graphics and Interactive Techniques (pp. 2563–2571). Association for Computing Machinery (ACM). https://doi.org/10.1145/3589334.3645572
  • The Impact of External Sources on the Friedkin–Johnsen Model / Out, C., Tu, S., Neumann, S., & Zehmakan, A. N. (2024). The Impact of External Sources on the Friedkin–Johnsen Model. In CIKM ’24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 1815–1824). ACM. https://doi.org/10.1145/3627673.3679780
    Download: PDF (2.69 MB)
  • Modeling the Impact of Timeline Algorithms on Opinion Dynamics Using Low-rank Updates / Zhou, T., Neumann, S., Garimella, K., & Gionis, A. (2024). Modeling the Impact of Timeline Algorithms on Opinion Dynamics Using Low-rank Updates. In Proceedings of the ACM Web Conference 2024 (pp. 2694–2702). ACM. https://doi.org/10.1145/3589334.3645714
    Download: PDF (1.4 MB)