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Department of Informatics Computation and Economics Research Group

Barna Pásztor


Barna Pásztor
Ph.D. Student
Department of Informatics
University of Zurich
Binzmühlestrasse 14
CH-8050 Zürich
Room BIN 2.B.05

Short Bio

Since September 2020, Barna is a Ph.D. student, jointly advised by Prof. Sven Seuken and Prof. Andreas Krause. He holds an MSc degree in Data Science from ETH Zurich and a BSc degree in Mathematics with Economics from University College London where he received the Dean’s Award in 2018. He also has two years of industry experience working as a Data Scientist at QuantumBlack, AI by McKinsey.

Research Interests

machine learning, artificial intelligence, multi-​agent reinforcement learning, complex systems, sociology, economics



  • Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning. Barna Pásztor, Ilija Bogunovic, Andreas Krause (2021). Preprint available on arXiv.
  • On the impact of publicly available news and information transfer to financial markets. Metod Jazbec, Barna Pásztor, Felix Faltings, Nino Antulov-Fantulin, Petter N. Kolm. R. Soc. open sci. 8 (2020): 202321.202321. Available at the publisher’s website.
  • Stochastic Gradient Descent Works Really Well for Stress Minimization. Katharina Börsig, Ulrik Brandes, Barna Pasztor. Graph Drawing and Network Visualization (2020). Pages 18-25. ISBN: 978-3-030-68766-3. Available on arXiv

Weiterführende Informationen


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