Jiarui Gan
Jiarui Gan
Wolfson Building, Parks Road, Oxford OX1 3QD
Interests
I work mainly in the area of computational game theory, and more broadly, in the fields of multi-agent systems, EconCS, and AI. My research focuses on questions about finding good strategies for interacting with intelligent agents, especially in real-world scenarios with all sorts of complexities. I am interested in exploring the power of computer science to tackle this challenge, and enjoy solving the related algorithmic "puzzles" and and uncovering what can and cannot be achieved through understanding the computational complexity of the problems. My work aims to deepen our understanding of strategic decision-making and multi-agent interactions, and to develop effective algorithms and models for these domains.
🔍 I'm looking for PhD students. Feel free to get in touch if you're interested! (See more about PhD at Oxford here.) For any application-related inquiries, please directly contact me (jiarui.gan@cs.ox.ac.uk) or the Department of Computer Science.
Biography
Prior to rejoining Oxford, I was a postdoctoral researcher at Max Planck Institute for Software Systems in Germany. I obtained my DPhil degree at Oxford in 2021.
Selected Publications
-
Bayesian persuasion in sequential decision−making
Jiarui Gan‚ Rupak Majumdar‚ Goran Radanovic and Adish Singla
In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22). Pages 5025–5033. 2022.
Details about Bayesian persuasion in sequential decision−making | BibTeX data for Bayesian persuasion in sequential decision−making
-
Schelling games on graphs
Aishwarya Agarwal‚ Edith Elkind‚ Jiarui Gan‚ Ayumi Igarashi‚ Warut Suksompong and Alexandros A Voudouris
In Artificial Intelligence. Vol. 301. Pages 103576. 2021.
Details about Schelling games on graphs | BibTeX data for Schelling games on graphs
-
Optimally deceiving a learning leader in stackelberg games
Georgios Birmpas‚ Jiarui Gan‚ Alexandros Hollender‚ Francisco Marmolejo‚ Ninad Rajgopal and Alexandros Voudouris
In Advances in Neural Information Processing Systems (NeurIPS'20). Pages 20624–20635. 2020.
Details about Optimally deceiving a learning leader in stackelberg games | BibTeX data for Optimally deceiving a learning leader in stackelberg games