Euan Ong

I am a final-year undergraduate at the University of Cambridge, studying Computer Science.

My ambition is to develop powerful, yet safe and interpretable abstract reasoners, whose internal state and behaviour remain transparent to the end user.

To this end, I'm particularly interested in exploring how the mathematical toolkits we use to understand and structure programs ‒ such as formal methods, types and category theory ‒ can inspire new ways to both reverse-engineer existing neural networks, and build scalable neurosymbolic systems.

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So far, my research has broadly focused on studying the behaviour of neural networks in vitro: understanding both how they generalise when learning to perform abstract tasks, and what this tells us about the algorithms they've learned in order to do so.

I'm currently probing the foundations of neural algorithmic reasoning, exploring attacks on vision-language models, and poking language model representations with a stick.

Published work

hijacks Image Hijacks: Adversarial Images can Control Generative Models at Runtime
Luke Bailey*, Euan Ong*, Stuart Russell, Scott Emmons (* denotes equal contribution)
Preprint, 2023
arXiv / project page + demo / tweeprint

We discovered that adversarial images can hijack the behaviour of vision-language models (VLMs) at runtime. We developed a general method for crafting these image hijacks, and trained image hijacks that force VLMs to output arbitrary text, leak their context window and comply with harmful instructions.

monoids Learnable Commutative Monoids for Graph Neural Networks
Euan Ong, Petar Veličković
Learning on Graphs, 2022
arXiv / reviews / project page / tweeprint

Using ideas from abstract algebra and functional programming, we built a new GNN aggregator that beats the state of the art on complex aggregation problems (especially out-of-distribution) while remaining efficient and parallelisable on large graphs.

Informal projects

attention heads Dissecting Deep Learning for Systematic Generalisation
Euan Ong, Etaash Katiyar, Kai-En Chong, Albert Qiaochu Jiang
Informal research, 2021

We investigated the capabilities of transformers to systematically generalise when learning to recognise formal languages (such as Parity and 2-Dyck), empirically corroborating various theoretical claims about transformer generalisation. Inspired by our observations, we derived a parallel, stackless algorithm for recognising 2-Dyck that could (in principle) be implemented by a transformer with a constant number of attention layers.

oxtd Object Detection in Thermal Imagery via Convolutional Neural Networks
Euan Ong, Niki Trigoni, Pedro Porto Barque de Gusmão
Technical report, 2019

We trained a Faster R-CNN object detection network to identify landmarks (e.g. doors and windows) in thermal images of indoor environments, with applications in the development of navigational aids for search and rescue operations.

Design inspired by Jon Barron's site.