Skip to content
Technical University of Munich

Anahit Sargsyan

Research Assistant, PhD Student

Uptown Munich-Campus D, Georg-Brauchle-Ring 60/62, Room 213 (2nd floor)

Office hours: by arrangement (Email)

E-mail: anahit.sargsyan(at)tum.de

 

Vita

Seit 01/2025Research Assistant at Technical University of Munich
Seit 09/2022PhD Candidate at the TUM School of Computation, Information and Technology
06/2022 – 12/2024Research Assistant at the Munich TUM School of Politics and Public Policy
10/2018 – 05/2022Assistant Instructor at the New York University, Abu Dhabi
07/2017 – 09/2018Visting Scholar at the Masdar Institute, Khalifa University
09/2015 – 09/2017Master of Engineering in “Industrial Engineering and Systems Management” at the American University of Armenia
09/2010 – 06/2014Bachelor of Philology at the Yerevan State University

 

Research activities

  • Social Network Analysis
  • Content Dissemination in Video Sharing Platforms
  • Human-bot Interaction
  • Data-driven Decision Support Systems
  • AI for Social Good

 

Publications

Pfeffer, J., Matter, D., & Sargsyan, A. (2023, June). The half-life of a tweet. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 17, pp. 1163-1167). https://doi.org/10.1609/icwsm.v17i1.22228

Makovi, K., Sargsyan, A., Li, W., Bonnefon, J. F., & Rahwan, T. (2023). Trust within human-machine collectives depends on the perceived consensus about cooperative norms. Nature Communications, 14(1), 3108. doi.org10.1038/s41467-023-38592-5

Abascal, M., Makovi, K., & Sargsyan, A. (2022). Unequal treatment toward copartisans versus non-copartisans is reduced when partisanship can be falsified (vol 16, e0244651, 2021). PLOS ONE, 17(2). https://doi.org/10.1371/journal.pone.0244651

Reichelt, M., Makovi, K., & Sargsyan, A. (2020). The impact of COVID-19 on gender inequality in the labor market and gender-role attitudes. European Societies, 23(sup1), S228–S245. https://doi.org/10.1080/14616696.2020.1823010

Sargsyan, A., Karapetyan, A., Woon, W.L., Alshamsi, A. (2020). Explainable AI as a Social Microscope: A Case Study on Academic Performance. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_24 

To top