Access to and use of space is of increasing national significance, but this technological advancement has become vulnerable to a critical self-inflicted threat: the presence of space debris from defunct hardware that contaminates our space environment, a high-altitude parallel to the human-induced climate change below. Harnessing the power of AI and machine learning, we will bring together Imperial’s expertise in relevant areas (e.g., data science, machine learning, sensors, networks, sustainability, space safety and security, and space engineering), and work with industrial partners and government stakeholders to help ensure the future of space is safe, secure and sustainable. Led by Dr Jonathan Eastwood, Professor Julie McCann, Professor Tom Pike, Dr Davide Amato, and Dr Mike Heyns. ...

This research is concerned with modern and smart radar system architectures for detecting, localising and tracking multiple targets in the presence of clutter, noise, interference and jammers. These radar architectures will be supported by novel and smart algorithms with super-resolution capabilities and maximum accuracy in estimating various parameters associated with the radar’s environment. RADAR is a classical defence and security subject. However, with the deployment of 5G+, a radar revolution is underway with many emerging applications beyond the “defence” and “security” domains. This research initiative is concerned with Multiple-Input Multiple-Output (MIMO) radar which has emerged as a leading candidate for stimulating major new advancements in radar theory and systems. Compared with a phased array radar, a MIMO radar can be viewed as its generalised form and provides an increase in the flexibility of using multiple probing signals at its transmitter. This waveform diversity offers a superiority in several fundamental aspects of radar’s functionality, including parameter identifiability (i.e., more targets can be identified) and enhanced performance of parametric estimation. Led by Professor Athanassios Manikas in collaboration with Dr Anders Silander, Saab.  ...

In collaboration with the KIOS Research and Innovation Centre of Excellence at the University of Cyprus, the research initiative aims to develop a cutting-edge, multidisciplinary centre for research in intelligent systems and networks for monitoring, control, and security of critical infrastructure systems. This research has the scientific and innovation potential to improve people’s lives and the economy in Cyprus and Europe. KIOS Centre of Excellence follows a holistic approach to the problem of monitoring, control, management, and security of Critical Infrastructure Systems (CIS) by bringing together a strong multidisciplinary team that spans all the required areas of expertise. Specifically, in addition to expertise in intelligent systems and control, sensor networks, optimization, machine learning, estimation, and fault diagnosis, the Centre of Excellence will also have extensive expertise in the operation, challenges, and modelling of different CIS (power networks, water distribution networks, transportation systems, telecommunication networks) and their interdependencies, cyber-security and protection of CIS, cognitive approaches for the human-computer interface, emergency response and decision making, cyber-physical systems and integration methods for hardware/software realization of the developed solutions. Led by Professor Thomas Parisini. ...

The Centre aims to serve as a platform for close, fruitful and long-term partnerships and collaborations among researchers from governments, industries and Imperial to formulate and perform defence research in the broad areas of communications and information technology. Prominent researchers at other leading universities will also be invited to participate. The Centre will encourage and perform blue-sky defence research and innovations through collaborations among multi-disciplinary researchers across organizational boundaries for cross-fertilisation. Current research areas of interest include: AI and autonomy Machine learning and optimisation Communications Computer and sensor infrastructures Wireless communications Blockchain technologies Quantum computing and communications Although its research is motivated by defence challenges and applications, the Centre promotes academic publications, application of developed technologies in civilian domains, establishment of startup companies, and training and nurturing of future research talents in the fields. Led by Professor Kin Leung. ...

Do you know the accurate CO2 footprint of a single thing around you? The table and chairs in front of you, the computer you sit at, your shoes perhaps? The answer is almost certainly ‘no’, as currently there is no adequate way for anyone whose goal is sustainable purchasing – consumers, procurement professionals or manufacturers – to instantly assess and rank the CO2 emissions of the goods they buy and sell. Nor is there a mechanism that companies can utilise to benchmark against one another in a way that is immediately visible to consumers, and which enables companies to gain a significant marketplace advantage when they improve the sustainability of their products and the way they transact business. We cannot expect consumers and industry to move towards a less carbon-intensive economy if they cannot judge the impact of their purchasing decisions. To address this, Imperial College London has devised a ground-breaking method – using Big Data and Machine Learning – to calculate the carbon emission of all consumer goods, incorporating their full-life cycle and enabling ranking comparable goods accordingly. Product life-cycle assessment is key to curbing global carbon emissions, as both the consumer products and services sectors account for 60% of all CO2 emission globally. The project, named Carbon Mark, is primed to create the capability to instantly calculate the carbon footprints of all consumer products worldwide. Led by Professor Nilay Shah, with Dr Eva Sevigne Itoiz and Dr Antonio Del Rio Chanona. ...

Fluid Dynamics lies at the heart of numerous important problems in climate and environmental sciences. For example, the atmosphere, ocean, cryosphere, and even the interior of the earth are all fluids that move on timescales ranging from seconds to hundreds of millions of years. Quantifying the exchange of anthropogenic heat and carbon between the atmosphere and ocean, or understanding the decline in sea-ice and land-ice in polar oceans and the subsequent sea-level rise, are all inherently fluid mechanical problems. On the environmental side, sea-level rise and coastal impacts, ocean plastics pollution as well as the effect of other pollutants such as oil spills, the harvest of ocean wave energy and urban pollution are some fluid dynamical applications. Tackling all of these problems rely on a number of shared tools that include high-performance computations, assimilations of observational data, machine and deep learning, and data visualization among others. The goal of this initiative is to bring together, through Imperial-X, scientists from various departments at Imperial College, along with partners in industry and government, whose research activities involve fluid dynamics and cutting edge data-driven methods. This will help cross-pollination of ideas, establish interdisciplinary cutting edge research that builds around the strengths of the College, and will help the College and the UK lead on high impact problems at the heart of climate change and environmental policy. Led by Dr Ali Mashayek. Other participants include Professor Darryl Holm, Dr Arnaud Czaja, Professor Martin Siegert, Professor Matthew Piggott and Professor Dan Crisan. ...

Earth science and engineering is at the core of several pressing societal questions. Climate change impacts the dynamics of our planet, from the atmosphere to the oceans and continents. The need to move away from fossil fuels means we will need to rely more on so called ‘Geo-energies’, such as hydrothermal heat, geological reservoir of hydrogen, and carbon capture and storage in the subsurface. And humanity’s constant pull towards exploration of the planets in our solar system also mean that an understanding of planetary processes is needed. Earth Data Science is characterized by challenging datasets: the data encompasses a large range of temporal and spatial scales, and it typically is large (‘Big Data’) and spatially sparse. Machine learning and deep learning approaches show great promises in this field, but optimal application of these approaches in Earth Data Science is not straightforward: this is what our proposal aims to address. Our research initiative leverages existing research and advances it by promoting integration across fields and across departments to answer relevant societal problems and builds upon our strong foundation of teaching master level courses in data science and machine learning. Led by Dr Cédric M. John. ...

Earth and planetary sciences are characterized by ‘Big Data’ capturing processes and products at a large range of temporal and spatial scales. 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. Machine Learning (ML) has attracted huge and ever-growing interest over recent years because it provides novel modelling frameworks. In ocean modelling, ML has been used for many purposes (e.g., observational data analysis, active flow control and shape optimization, reduced-order modelling). Its promising applications for parameterizing mesoscale oceanic currents and eddies remain in infancy, although with huge research potential. Our project enriches ML methods with ideas from dynamical systems to demonstrate the power and utility of the hyper-parameterization approach — a new paradigm for modelling turbulent ocean flows. Led by Professor Pavel Berloff. ...

The focus of this centre is to combine physics modelling with AI technologies for grand challenges in scientific fields such as the environment, sustainable energy, health and geophysics. We aim to promote and lead scientific advances and technological innovations through advanced machine learning, data assimilation, sensitivity/uncertainty/error analysis, design optimisation and control, and computational modelling. Our vision is to provide the international leadership in AI modelling as a strategic resource for scientific education, research (development) and inquiry. Led by Professor Christopher Pain. ...

We bring together diverse analysts and experimentalists to create a best practice hub in single cell data analysis. We will tackle challenges in inference of lineage, cellular state and dynamics, spatial structure, experimental design and the use of cohorts. Our team is composed of experts in machine learning, statistics and stochastics combined with focuses in sequencing and the study of themes from immunology to neurodegeneration. We are linked to the Centre for the Mathematics of Precision Healthcare. Our Team Mathematics: Mauricio Barahona, Barbara Bravi, Marina Evangelou, Sarah Filippi, Nick Jones, Anthea Monod, Vahid Shahrezaei, Philipp Thomas Life Sciences: Ruben Perez-Carasco Medicine: Alexis Barr, Luca Magnani, Samuel Marguerat, Paul Matthews, Iain McNeish, Michela Noseda, Nathan Skene ...

The value of engineering approaches to biology is amply demonstrated: understanding virus interactions and engineering vaccines; engineering CAR-T cells equipped with sensing and logic devices to precisely tune their immunotherapeutic response; sustainable bio-based production of chemicals to minimise environmental impact. To tackle the complexity of biological systems design and achieve even more ambitious results, more reliably and in less time, we need an innovative new approach, so that bio-based manufacturing and engineered cell therapies are the norm instead of the exception. 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.Recently, this research initiative received an award of £1.5 m of funding as part of the AI-4-EB consortium, which aims to further research in the intersection of Artificial Intelligence and Engineering Biology. Led by Professor Geoff Baldwin and Professor Guy-Bart Stan. ...

In a world where the desire – and the need – to interact with computer-based systems is a daily-life experience, machine learning has a crucial role in maximizing the accessibility and functional effectiveness of advanced systems. We foresee the increasing importance of augmented reality (AR) as a way to tap into human perception in a wide range of applications, from healthcare to entertainment to defence. We foresee ‘hearing aids’ evolving into ‘assistive hearing devices for all’. We foresee AR-enabled glasses and acoustically transparent spatial sound rendering. We foresee acoustic scene analysis and synthesis leading to a new generation of audio enhancement technologies. The voice and audio capabilities of such AR systems, together with audio-visual fusion, currently present many technical challenges in analysis and rendering. Such challenges need to be solved to reach the next level of effectiveness. Our research aims to overcome key components of the AR technical challenges using machine learning techniques in a multidisciplinary research mindset. Led by Professor Patrick Naylor, Dr Lorenzo Picinali and Dr Dan Goodman ...

This initiative provides space for collaborative research projects related to AI for image analysis with a focus on clinical applications in radiology. The initiative leverages the strong collaborations between Engineering, Medicine, and our industrial partners, providing a new way for interactions in a shared lab space. The initiative offers opportunities for inter-disciplinary co-working, joint seminars, journal clubs, and real-world demonstrations of AI imaging technology. Led by Dr Ben Glocker, Dr Islem Rekik, Dr Chen Qin, Dr Bernhard Kainz, Professor Daniel Rueckert, and Dr Wenjia Bai.  ...

Ovarian masses are very common, affecting 14% of women. Although the incidence of ovarian cancer is low, it remains the leading cause of death from a gynaecological malignancy in the UK (Cancer Research UK). Ultrasound is recognised as first line imaging for ovarian masses globally. The differentiation between a benign and a malignant mass can be challenging. Diagnostic uncertainty in ovarian mass classification can lead to additional investigations such as MRI and surgical intervention, due to potential concern about malignant potential. Our current project, in collaboration with Professor Dirk Timmerman and his team at UZ Leuven, focuses on applying ML clinically to improve the level of diagnostic confidence and provide practitioners with an artificial second reader that encodes representations from thousands of examinations, which is often more than a radiologist sees during their career. Furthermore, a ML model, capable of extracting specific features from within an ultrasound image could support the training of basic/intermediate ultrasound scanners. Led by Mr Srdjan Saso. ...

Our research is focused on using machine learning to discover mechanisms that underpin common cardiovascular diseases by integrating data from human imaging, genetics and environmental risk factors. This work includes developing algorithms for predicting human survival from cardiac motion and understanding how complex traits influence the risk of heart failure. We also collaborate with industry to accelerate progress in drug discovery using automated genotype-phenotype modelling. Led by Professor Declan O’Regan. ...

The Digital Health research programme will focus on responsible use of modern technologies to improve health and wellbeing, including but not limited to wearables, mobile apps, conversational systems, and home monitoring systems. Application areas include health promotion, supporting behavioural change, managing long-term conditions, as well as developing trustworthy AI-based decision support tools for personalised health. At present, digital population technologies are showing positive results in clinical trials but need larger effect sizes and more flexibility to achieve substantial population health gains. Working with collaborations enabled by testbed capabilities, joint studentships, and the Imperial-X environment, we want to develop multidisciplinary programmes in both low and high technology readiness areas. We already have active projects on wearable cardiovascular monitors, in-home monitoring, as well as diabetes prevention, and scope to rapidly develop new projects. Led by Dr Aldo Faisal. ...

This research initiative, in association with the UK Dementia Research Institute, will develop fundamental building blocks for the analysis of digital biomarkers in healthcare applications. It will focus on developing modular and adaptive algorithms and tools that use sensory observation, measurement data and healthcare records to extract actionable information for effective and timely interventions. Recent developments in the Internet of Things (IoT) combined with the growing adoption of wearable devices provide an unprecedented opportunity to collect and process data from living and working environments and vital body signals in (near-) real-time. These new forms of continuous data have the potential to transform remote and in-home monitoring for long-term conditions such as dementia and other neurodegenerative conditions, traumatic brain injury and ageing-associated comorbidities. Led by Dr Payam Barnaghi. ...

Currently, most surgical procedures and equipment are described with the use of a written operative technique manual with the aid of diagrams and surgical workflow. There is some use of audio-visual content, but these are not standardized. These manuals are often difficult to use, and surgeons are left depending on company representatives and demonstrations prior to performing the operation. We have shown from our research that Cognitive Task Analysis (CTA) based learning tools are a validated, evidence-based method of describing a surgical technique with the use of innovative multimedia platforms. These are easy to use and allows repeated sustained practice by any surgeon around the world from the comfort of their own home which minimizes some of the major drawbacks of other simulation modalities. Our vision is to have a CTA based tool for every surgical operation so that surgeons have a clear idea as to the technical steps they need to perform as well as a cognitive understanding behind these steps in order to reduce potential errors and help surgeons innovate solutions for each phase of a surgical procedure. Led by Mr (Dr) Chinmay Gupte.     ...

This research initiative aims at achieving unprecedented resolution using miniature size sensor arrays. In electromagnetic and acoustic sensing, physical size of sensor arrays limits their angular resolution. In this research, different frequency signals are used to achieve Synthetic UPsampling ARray (SUPAR) super-resolution, which allows us to improve angular resolution in multitude. Artificial intelligence (AI) tools will be employed for real time probing design and imaging, paving the way for commercial adoption. The output of this research is expected to make significant impacts for 1) sensing using mobile platforms (e.g. vehicles) where physical space is a precious resource;  2) see-through imaging where low frequency signals are typically employed for signal penetration capability but could lead to low resolution; 3) high resolution functional imaging where scenes change rapidly.  Led by Dr Wei Dai. ...

This research 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. Of particular interest is investigating novel techniques for multimodal imaging and AI based analysis of bio-physical properties of subsurface subject/material appearance for wide-ranging applications in computer graphics and vision and beyond. A key novel approach will be to combine practical, multimodal imaging of bio-physical appearance properties with differentiable inverse rendering and deep learning techniques to tackle hard inverse problems of appearance analysis from sparse, possibly incomplete/mixed observations. Led by Dr Abhijeet Ghosh. ...

The Sustainable Tech Lab will be a multidisciplinary academic biome; creating a focal point for the delivery of sustainable economic, social and industrial technology enabled solutions for the future global economy. As the world gravitates away from a carbon intensive economic model to a more sustainable one, the most effective solutions will be those which have been developed through the Sustainable Tech Lab’s rigorous multidisciplinary academic engine. At the heart of the Sustainable Tech Lab is the belief that ESG metrics — traditionally challenging to formalize, measure, and validate procedures — can be effectively designed and implemented through the creation of disruptive technologies powered by AI/ML to support the transition to regenerative economies. In addition to research output, the Sustainable Tech Lab will provide a coordinated platform for thought leadership to fellow academics, governments and industry practitioners. By initiating an active engagement programme, we seek to enable better corporate environmental disclosure, more effective governmental policy making and sustainable profitability. Led by Dr Enrico Biffis and Professor Deeph Chana. ...

The Resources Observatory (RO) aims to develop and maintain a digital twin of the physical economy, and to use this quantitative evidence to inform policy-making and business strategy that relate to emerging resource issues (e.g., 2021 semiconductor chip shortage, rare earth crisis) and transitions (e.g., internal combustion to electric vehicles). The digital twin relies on Element Tracker, a novel material flow analysis model. Element Tracker is built on Bayesian principles and can account for poor data availability. Element Tracker also builds on a novel database architecture designed to assimilate resource flow data across economic scales. RO will deliver a step change in methodology, improved understanding of the effects of technology, businesses, behaviour, and policies on systemic resource, social, environmental, and economic issues and boosts to society’s (particularly for national policy-making) and business capabilities to address these issues. This year Dr Myers has become Co-I on a new grant, and the postdoc employed in his department is developing methodology (Bayesian Material Flow Analysis) and code that will underpin a significant chunk of I-X related work. Dr Myers also secured add-on funding. In combination with the grant shared by researchers from the UKRI CE Centre (UCL, British Geological Survey) and the Office of National Statistics, Dr Myers and his colleagues will apply this new methodology to uncover insights about how the UK can mitigate supply risk, deliver on upcoming infrastructure/building programs, and reduce environmental impact towards a circular economy over an 8-month period from May-December. Led by Dr Rupert Myers, Dr Yves Plancherel, Dr Pablo Brito-Parada and Dr Kolyan Ray. ...

The shift to a digital society and a strong focus on data-driven research has put the entire computing stack as the centrepiece of interdisciplinary research and innovation. The current wave of AI and machine learning advances is an excellent example of this. It was enabled by advances in computer hardware, data centre design, parallel programming and distributed systems. Consequently, computing infrastructure and software systems will play a crucial role in future engineering and natural sciences research and industry. This trend will continue to drive the massive growth in data and compute requirements, which is predicted to reach 400 exabytes per day by 2025 (the equivalent to 100 million of today’s average enterprise disk drives per day). Dealing with such tremendous amounts of data brings many exciting challenges in performance, energy efficiency, security and productivity. This research initiative aims to ensure that computing infrastructure and software systems can act as the foundation for addressing scientific, societal and environmental challenges. As such, it will play a central role in Imperial’s Systems Research initiatives. Members of the initiative: Dr Thomas Heinis, Prof Paul Kelly, Prof Wayne Luk, Prof Jenny Nelson, Prof Peter Pietzuch, Dr Holger Pirk and Dr Lluis Vilanova. ...

Intelligent Transmission and Processing Laboratory (ITP Lab) was established in 2020 when Professor Geoffrey Li joined Imperial College London. ITP Lab is interested in fundamental and application issues on artificial intelligence for signal processing and communications in general. The past research areas include blind signal processing, OFDM and MIMO for wireless communications, cross-layer optimization for SE and EE wireless networks, cognitive radio and D2D networks, TeraHertz and mmWave communications, and V2X and UAV vehicular communications. Currently, ITP Lab is working on the following topics: • Fundamental issues in machine learning, compressive sensing, and optimization. • Dynamic, accretionary, and meta-learning with applications. • Deep learning for physical layer processing in communications. • Intelligent wireless resource allocation. • Federated learning related issues. Led by Professor Geoffrey Ye Li. ...

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. Traditional computer systems deploy hardware and provide tools to enable self-optimisation and update. But this evolution dynamic is in software – the systems does not evolve its hardware. The Emergent Things programme, leveraging connections in the departments of Computing, EE, and Design, are investigating a wholly new approach to system design that both emerges its architecture, hardware, software, and indeed the intelligence infrastructure to underpin this. Our initial focus is on Networked embedded sensor-based systems (NESS). While in-situ a basic NESS can evolve its improved operation through learning its context, etc. This clearly requires AI to learn and then enact changes, and indeed learn from the changes made. This is already difficult in software-based systems, hardware brings a real challenge. To address this a more lateral view is required to answer major research questions like: How do we translate what the user wants to the system that is (self) delivered? How do we design devices that are not only printed on demand, but have onboard printing facilities to update hardware? What does an Operating System for this even look like? Is a cross-layer communications approach the best abstraction to achieve agility, resilience, and longevity? Can this system’s intelligence grow and update, can it self-protect? Is that a good thing or a bad thing? In answering these, we will explore a completely new way to design computers. Led by Professor Julie McCann, Dr Dalal Alrajeh and Dr David Boyle. ...

The impact of quantum information can be both constructive and destructive: one the one hand quantum parallelism offers unprecedented computational power; on the other hand it can be used to break cryptosystems. QUAIL is uniquely positioned to address both sides of quantum information science in a holistic approach, namely, not only harnessing the power of quantum computers for artificial intelligence, but also securing communications and computation for machine learning under the threat of quantum computing attacks in the post-quantum era. Lattice-based cryptography is a highly active area of post-quantum cryptography research. Regular meetings to learn about current research in the field are held to keep up to date with developments in the field and encourage discourse with other researchers. Information for the next Lattice Coding & Crypto meeting is included on this page.  Led by Dr Roberto Bondesan and Dr Cong Ling ...

We are at the dawn of the Age of Artificial Intelligence. The scope and scale of impact of AI is predicted to be more profound than any other period of technological, social and personal transformation in our history. AI has the potential to radically transform every industry and every society, including the very definitions of our identities. We believe that AI can help address the needs of humanity, in healthcare, in wellbeing and towards sustainable societies in general. However, to this end, AI must incorporate an understanding of what makes us humans tick — physiologically, neuroscientifically, cognitively and psychologically. We propose to capture real-world human behaviour that can drive Human AI research by tapping into the expertise gathered within I-X and the opportunity presented by the new campus. The White City campus presents the possibility of not only having conventional, indoor, sensorised living  labs, but also to go beyond and treat the campus as a large indoor and outdoor living lab that can capture a diverse collection of our daily living activities, behaviours, and states. These can be collected through advanced technology, be it sensors, high-speed wireless communication networks, IoT devices, or self-reports through opt-in apps and interactive devices, and be stored anonymously in secure storage to protect the privacy of participants. The White City-wide living lab can also serve AI research beyond the examples above, such as making mass data available on power usage and allowing for experiments on smart power management systems that aim to adapt to human users, but also to nudge users towards more efficient energy use patterns, useful in smart cities and smart living research.  Led by Dr Aldo Faisal ...

Interfacing with the human nervous system is at the core of emerging technologies in academia and industry (e.g., Neuralink, BrainGate, Ctrl-Labs/Facebook Reality Labs). Its ultimate goal 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. This achievement would have profound implications for human-machine interaction technologies, such as for the control of robotic limbs or for navigating in virtual reality (VR) environments for entertainment, telemedicine, or computer interaction. In addition, it would provide a breakthrough in our understanding of the working principles of the central nervous system, potentially enabling new cures for currently incurable neurological diseases, such as Parkinson’s disease. Led by Professor Dario Farina ...

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. Advances in AI and Machine Learning have achieved a transformative impact in many domains, bringing unprecedented paradigm shifts to many sectors. However, their increasing use in high-stake and mission critical tasks, where wrong decisions may cause unintended, dangerous, or catastrophic consequences, requires more than ever the need for intelligible AI (IAI) solutions that are robust, explainable, understood and trusted by humans. Domains such as healthcare, policing, business critical systems, security and defence, and complex systems engineering are increasingly demanding for greater intelligibility of AI and Machine Learning (ML) systems. Led by Professor Alessandra Russo ...

We are increasingly surrounded by, and interacting with AI models and smart devices running these models and applications. In this initiative we will investigate the security risks and privacy threats from this pervasive monitoring and analytics ecosystem. Traditional methods for ensuring the correctness and reliability of our software systems, such as informal prose specification and ad-hoc validation, are no longer adequate for our modern software systems. 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. As we are surrounded by an increasing array of heterogeneous and untrusted devices, collecting, analysing, and transmitting data from our most private moments and living spaces, we need to develop mechanisms for identifying, and mitigating, the privacy and security threats emerging from this new cyber-physical world around us. The work in this initiative links with the ongoing initiatives such as Safe AI, Cyber Physical Systems, and create a bridge with the ongoing efforts in the Institute for Security Science and Technology. Led by Dr Hamed Haddadi ...