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

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/.

Role

Assistant Professor
Machine Learning, E194-06

Contact

Courses

2024W

Bachelor Thesis in Computer Science / 194.112 / PR
Fundamentals of Digital Systems / 192.134 / VU
Project in Computer Science 1 / 194.145 / PR
Seminar for PhD Students / 194.110 / SE

2025S

Bachelor Thesis in Computer Science / 194.112 / PR
Project in Computer Science 1 / 194.145 / PR

Projects

Towards Trustworthy Recommendation Systems for Online Social Networks
2023 – 2030 / Vienna Science and Technology Fund (WWTF)

Publications

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. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 8(3), 2563–2571. 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)

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