Paul Friedrich

I am a Computer Science PhD student in the Department of Informatics at the University of Zurich, co-advised by Sven Seuken and Giorgia Ramponi. I am associated with the ETH AI Center. In my research, I am interested in using machine and reinforcement learning to analyze and shape equilibria in games and marketplaces, and design more fair and efficient mechanisms.

I hold a Master's (2020) and Bachelor's (2018) degree in Mathematics from ETH Zurich, advised by Josef Teichmann. There, I focused on probability theory, mathematical finance, and machine learning. In 2017, I studied abroad at the Hong Kong University of Science and Technology (HKUST).

During and after my studies, I worked as a consultant in the risk management practice of Ernst & Young Zurich. Outside of work you can catch me running or enjoying pretty much any sport on, in and below water.

If you are offering a research internship for which I could fit well or are interested in collaborating, please reach out!

Email  /  CV  /  Google Scholar  /  Github  /  LinkedIn

Publications

Learning Collusion in Episodic, Inventory-Constrained Markets
Paul Friedrich, Barna Pásztor, Giorgia Ramponi
Preprint (paper / github), previously at Agentic Markets Workshop @ ICML '24, 2024 (paper)

Scalable Mechanism Design for Multi-Agent Path Finding
Paul Friedrich*, Yulun Zhang*, Michael Curry, Ludwig Dierks, Stephen McAleer, Jiaoyang Li, Tuomas Sandholm, Sven Seuken
IJCAI, 2024 (paper / github / talk slides)

Market Design for Drone Traffic Management
Sven Seuken, Paul Friedrich, Ludwig Dierks,
AAAI, 2022 — Won 3rd best paper in its category (paper)

Machine-Learning enhanced Market Design for Drone Traffic Management
Paul Friedrich, Sven Seuken, Ludwig Dierks
Working paper

Design from Jon Barron's website