Closed-loop Interpretable AI for Biological Systems

Privacy and Security for Human Centred AI Explainable, Safe & Robust AI

In this research theme we will investigate the privacy threats and security risk and solutions facing our personal data and devices around us in the next decade.

The purpose of the SaSSy Initiative is to bring rigorous, scientific methods to the design and analysis of our modern software systems, to set new standards of rigour and propose new ways to validate emerging AI-driven systems which are not catered for by current approaches.

This is a multidisciplinary research initiative for the development and delivery of Intelligible AI (IAI), a new generation of AI systems that brings together interpretable, explainable and safe AI technologies to help develop IAI-empowered systems that are understood and trusted by humans.

We propose to capture real-world human behaviour that can drive Human AI research.

Human Interfacing Humans & AI

The ultimate goal of this research is the establishment of a stable connection to human neural cells, and the decoding and interpretation of neural activity patterns associated with human behaviours and intent.

The vision of QUAIL is to become one of the UK’s leading research labs in quantum/post-quantum technology and artificial intelligence, by bringing together academic and industry partners to tackle major challenges in this emerging area.

Emergent Things Sensing & AI

Networked embedded sensor-based systems can self-repair and self-update, in terms of both their hardware and software infrastructures. In doing so, this initiative investigates new ways to co-specify systems architectures, learn new software-hardware designs for 3D printing, and evolve new operating systems.

Our lab is interested in fundamental and application issues on artificial intelligence for signal processing and communications in general.

This research initiative aims to ensure that computing infrastructure and software systems can act as the foundation for addressing scientific, societal and environmental challenges.

Resources Observatory (RO) I-X Economy

RO aims to develop and maintain a digital twin of the physical economy, and to use this quantitative evidence base to inform policy-making and business strategy.

Sustainable Tech Lab I-X Economy

The Sustainable Tech Lab (STL) will be a multidisciplinary academic biome; creating the focal point for the delivery of sustainable economic, social and industrial technology enabled solutions for the future global economy.

Training Safer Surgeons I-X Health

Wearable technology in knee braces is a promising new approach to provide biometric feedback that may allow patients to rehabilitate more effectively from knee injuries and after knee surgery.

Machine learning (ML) has enormous potential to act as a diagnostic adjunct within gynaecology to tackle two main issues: diagnostic uncertainty and delayed diagnoses.

We use computer vision techniques to analyse cardiac imaging in both health and disease. We use these models to understand the genetic basis of heart failure and to predict future health outcomes.

This initiative aims at using different frequency signals to achieve Synthetic UPsampling ARray (SUPAR) to improve angular resolution in multitude. Artificial intelligence (AI) tools will be employed for real time probing design and imaging.

The Colin Cherry Laboratory I-X Health

“Human and machine auditory perception” Research into intelligent spatial audio and human perception using machine learning, neuroscience, signal processing, and applied mathematics.

Digital Health I-X Health

We believe that AI can help address the growing and unmet global needs for highperformance healthcare and wellbeing and lead us towards smarter, sustainable societies in general.

The Research Initiative will focus on combining novel computational imaging methods with AI and deep learning techniques to address previously unsolvable or analytically intractable inverse problems in diverse fields such as realistic computer graphics, computer vision, signal processing, medical imaging and natural/physical sciences.

This initiative brings together researchers and scientists in the field of machine learning based image analysis. The aim is to develop new computational tools for improving image-based detection and diagnosis of disease.

This research will focus on developing modular and adaptive algorithms and tools that use sensory observation and measurement data and healthcare records to extract actionable information for effective and timely interventions.

Our initiative develops advanced single cell data analysis of disease: this is as a unifying focus for efforts across Imperial. By converging theorists, computer scientists, technologists and experimentalists with medical and industrial challenges we seek a new approach to bioscience from the fundamentals of cellular characterisation upwards to precision healthcare.

Using interpretable Artificial Intelligence (AI) to direct the design and implementation of new biological systems is a radical approach that will transform our ability to predictably engineer biological systems.

Our goal is to use cutting edge data science, machine learning and deep learning to better characterize and predict complex planetary processes in order to address societal problems such as climate change and sourcing clean energy.

Our vision is to provide the international leadership in AI modelling as a strategic resource for scientific education, research (development) and inquiry.

Our goal is to use cutting edge data science, machine learning and deep learning to better characterize and predict complex planetary processes in order to address societal problems such as climate change and sourcing clean energy.

Carbon Mark I-X Planet

We aim to calculate the carbon emission of all consumer goods, incorporating their full-life cycle and enabling ranking comparable goods accordingly.

Research involving fluid dynamics and cutting edge data-driven methods.

Developing a cutting-edge, multidisciplinary Centre of Excellence in intelligent systems and networks for monitoring, control, and security of critical infrastructure systems.

Modern and smart radar system architectures for detecting, localising and tracking multiple-targets in the presence of clutter, noise, interference and jammers.

Formulating and performing defence research in the broad areas of communications and information technology.

IXS: Imperial in Space I-X Space

Harnessing the power of AI and machine learning, we will bring together Imperial’s expertise in relevant areas.

Closed-loop Interpretable AI for Biological Systems

Our research