Michael’s research is at the intersection of machine learning, reasoning and natural language understanding, with an additional focus on achieving the best social and civilisational impacts of increasingly powerful AI.

Alex’s research focuses on NLP tasks and automated reasoning with neural networks.

Yang’s research focus is on applying reinforcement learning to automatic reasoning and proving.

Transformer-based Semantic Parsing of Building Regulations: Supporting Regulators in Drafting Machine-Interpretable Rules

Assessing and Enhancing Language Models for Complex and Robust Logical Reasoning over Natural Language

Inductive Reasoning over Multiple Texts for Reading Comprehension

Echoes of Authenticity: Exploring the Frontiers of Copyright Protection in Music AI with Watermark Technologies and Adversarial Machine Learning

Robust reasoning in Deep Learning engines with Causal Graph modelling

Teaching Smaller Language Models To Generalise To Unseen Compositional Questions

Generating Optimal Path Subgoals through Supervised Learning

General-purpose time series modelling for interpretable and domain-agnostic time-series analysis

Explaining State-of-the-Art Models as Approximations of Unbounded Ideals

Integrating Insights from Human Cognition with Artificial Neural Networks to Improve Data Efficiency During Learning

Leveraging Linguistic Knowledge for Low-resource Natural Language Processing