Projects

An archive of older projects can be found here.

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Master’s Dissertation supervised by Prof Pietro Liò, Dr Dominique Beaini and Hannes Stärk – We propose GraphAC (Graph Adversarial Collaboration), a conceptually novel, principled, task-agnostic, and stable framework for evaluating GNNs through contrastive self-supervision, without the need of handcrafted augmentations.
Deep Reinforcement Learning for Mahjong
Bachelor’s Dissertation supervised by Dr Sean Holden – We present Meowjong, an AI for 3-player Mahjong (Sanma) using deep reinforcement learning, with an informative and compact 2-dimensional data structure for encoding the observable information in a Sanma game.