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Our Research

Our researchers

MINDS researchers have gone on to postdoctoral positions, industry roles and enterprise activity across the AI and electronics sectors. Their profiles below give a picture of the range of research carried out through the CDT and the paths it has opened up.

Agent-Based Adaptive Systems

Decentralised AI for Situational Awareness

Thomas Kelly

Game Theory and Multi-Agent Systems to Detect and Respond to Cyber Intrusions in IoT Environments

Charles Hutchins

Reducing Carbon Emissions in Shipping: Tax Incentives, Optimisation and Prediction

Matthew Durrant

Emergent Communication in Social Games

Olaf Lipinki

A Neuro-Symbolic Approach to Explainable Reinforcement Learning

Mark Towers

Nature-Inspired Communication in Reinforcement Learning Systems

Jennifer Barnes-Nunn

Efficient agent training from Human Preferences and Justifications in safety-critical environments

Ilias Kazantzidis

Interactive Lifelong Reinforcement Learning

John Birkbeck

Interactive Lifelong Reinforcement Learning

Kryspin Varys

Radar Imaging for Non-Invasive for Brain Imaging and Anamoly Detection

Radar Imaging for Non-Invasive for Brain Imaging and Anamoly Detection

Intelligent Coordination and Planning for Human-Robot Swarms

Intelligent Coordination and Planning for Human-Robot Swarms

Sample-Efficient Exploration in Visually Complex Reinforcement Learning Tasks

Sample-Efficient Exploration in Visually Complex Reinforcement Learning Tasks

Multi-modal event classification of social media content where training data is limited and/or emerging

Multi-modal event classification of social media content where training data is limited and/or emerging

Embedded Artificial Intelligence

Achieving Progressive Intelligence for Low Power Systems

Jack Dymond

Relative Movement Quantisation for Lossless Network Compression

Christopher Subia-Waud

Binaural Machine Listening in the Cocktail Party Scenario

Hsuan-Yang Wang

Graph Neural Networks for Robotic Grasping

Kian Spencer

Convolutional Neural Networks for Auditory Attention in Robots

Elliot Stein

Efficient learning on resource-constrained embedded systems

Epifanios Baikas

Machine learning for the analysis of ambient marine noise

Konstantinos Drylerakis

Federated Learning on Embedded Systems

Shannon How

Modelling Quantum Chromodynamics with Geometric Deep Learning

Kieran Maguire

Secure and Resilient Hardware Implementation of AI Modules

Arezou Nayebi

REACTOR: Robust Embedded Adaptive Control Techniques for Online Reconfiguration

Carl Richardson

Disentangled Representation Learning with Applications in Marine Environments

Nikos Chazaridis

Creating Augmented Reality Soundscape Experiences on Edge Devices

Creating Augmented Reality Soundscape Experiences on Edge Devices

Efficient Visual Place Recognition for Real-Time Visual Perception

Efficient Visual Place Recognition for Real-Time Visual Perception

Nanoelectronic Technologies for AI

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Secure Computing Systems Based on the Stochasticity of RRAM (memristor-based memories)

Callum Aitchison

AI Hardware Accelerator: Encoding Multi-timescales in Artificial Synapses

Alexander-Hanyu Wang

Symbolic AI provides a pathway towards achieving higher level intelligence

Alexander Lowe

Task-Optimised Devices and Systems

Efficient DNNs for Inference at the Edge

Sulaiman Sadiq

Assisted Predistortion for Wireless Communications

Jack Williamson

Fault Prediction Using Digital Twin Techniques

Matthew Pugh

Machine-Learning based Hierarchical Modulation based Cooperative Non-Orthogonal Multiple Access

Harry Horler

Search Publications

Latest Publications

Efficient Federated Learning: a Tsetlin Machine-based approach - Shannon Shi Qi How

Type: Thesis | 2026 | University of Southampton | Item availability restricted.

Dataset in support of the thesis 'Efficient Federated Learning: A Tsetlin Machine-Based Approach

Type: Dataset | 2026 | University of Southampton | Item availability restricted.

Dataset supporting thesis titled: "Exploring temporal dynamics of TiOx-based memristors for optimised robustness in neuromorphic computing"

Type: Dataset | 2026 | University of Southampton

Analysis of Lurie systems for learning and control - Carl Robert Richardson

Type: Thesis | 2026 | University of Southampton

Analysis of Lurie systems with magnitude nonlinearities and connections to neural network stability analysis - Carl Robert Richardson, Steve Gunn and Matthew Turner

Type: Article | 2026

Olaf Lipinski

MINDS alumnus. Researched how AI agents develop languages from scratch, focusing on how they communicate about time. Now Senior Machine Learning Scientist at Visa and Visiting Fellow at Southampton.