Jiarui Gan

Jiarui Gan

Departmental Lecturer

E: jiarui.gan@cs.ox.ac.uk

Wolfson Building, Parks Road, Oxford OX1 3QD

Themes:

• Artificial Intelligence and Machine Learning
• Algorithms and Complexity Theory

See Also:

• Personal website

Interests

I work mainly in the area of computational game theory, and more broadly speaking my interests lie in the fields of multi-agent systems, EconCS, and AI. The central topic of my research is about finding good strategies to use when interacting with intelligent agents (including AI-powered autonomous agents or human beings), especially in real-world scenarios with all sorts of intricacies. I'm fascinated by the power of computer science in tackling this task, and I enjoy solving the related algorithmic "puzzles" and uncovering what can and cannot be achieved through understanding the computational complexity of the problems.

✦ I'm looking for PhD students. Feel free to get in touch if you're interested! (See more about PhD at Oxford here.) 

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

View AllManage 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