Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

This project is about devising and implementing a smart operating room environment, powered by trustable, human-understanding artificial intelligence, able to continually adapt and learn the best way to optimise safety, efficacy, teamwork, economy, and clinical outcomes. We call this concept MAESTRO. A fitting analogy for MAESTRO is that of an orchestra conductor, a 'maestro', who oversees, overhears and directs a group of people on a common task, towards a common goal: a masterful musical performance. Although the music score is identical for all orchestras, there is no doubt that they all perform it in different ways and some significantly better than others. Although the quality and personality of orchestra musicians is very important, it is widely accepted that the role of the maestro is crucial, and extends beyond the duration of the musical performance to rehearsals and understanding of the context behind the music score. Thus, while it is possible for orchestras to perform without conductors, most cannot function without one. Our proposed MAESTRO AI-powered operating room of the future rotates around four key elements: (a) The holistic sensing of patient, staff, operating room environment and equipment through an array of diverse sensor devices. (b) Artificial intelligence focused on humans (human-centric), able to continually understand situations and actions developing in the operating room, and of intervening when necessary. (c) The use of advanced human-machine user interfaces for augmenting task performance. (d) A secure device interconnectivity platform, allowing the full integration of all above key elements. As in our orchestra analogy, our envisioned MAESTRO directs the OR staff and surgical devices before, during and after a surgical procedure by: (1) Sensing surgical procedures in all their aspects, including those which are currently neglected such as the physiological responses of staff (e.g., heart rate, blood pressure, sweating, pupil dilation), focus of attention, brain activity, as well as harmful events that may escape the attention of the clinical team. (2) Overseeing individual and team performance in real-time, throughout the operation and across different types of surgeries and different teams. (3) Guiding and assisting the surgical team via automated checkpoints, virtual and augmented visualisations, warnings, individualised and broadcasted alerts, automation, semi-automation, robotics, and other aids and factors that can affect performance in the operating room. (4) Augmenting and optimising individual and collective operational capabilities, skills, and task ergonomics, through novel human-machine interaction and interfacing modalities. The project is designed to have a significant societal, economic and technological impact, and to establish the NHS as a leading healthcare paradigm worldwide. MAESTRO leverages the expertise of top researchers in the areas of robotics, sensing, artificial intelligence, human factors, health policies and patient safety. It is co-designed in collaboration with top clinicians, one of the largest NHS Trusts in England, patient groups, performing artists, and several small and medium-sized enterprises and large multinational industries operating in the areas of artificial intelligence, medical devices, digital health, large networks, cloud services, cyber security.

Vision: In this fellowship, I aim to address a major challenge in the adoption of user-centred privacy-enhancing technologies: Can we leverage novel architectures to provide private, trusted, personalised, and dynamically- configurable models on consumer devices to cater for heterogenous environments and user requirements? Importantly, such properties must provide assurances for the data integrity and model authenticity/trustworthiness, while respecting the privacy of the individuals taking part in training and improving such models. Innovation and adoption in this space require collaborations between device manufacturers, platform providers, network operators, regulators, and the users. The objectives of this fellowship will take us far beyond the status-quo, one-size-fits-all solutions, providing a framework for personalised, trustworthy, and confidential edge computing, with ability to respect dynamic policies, in particular when dealing with sensitive models and data from the consumer Internet of Things (IoT) devices. In this fellowship, I aim to address these challenges by designing and evaluating an ecosystem where analytics from, and interaction with, consumer IoT devices can happen with trust in the model and authenticity, while enabling auditing and personalisation, hence pushing today's boundaries on all-or-nothing privacy and enabling new economic models. This approach requires designing for capabilities beyond the current trusted memory and processing limitations of the devices, and a cooperative dialogue and ecosystem involving service providers, ISPs, regulators, device manufacturers, and the end users. By designing our framework around the latest architectural and security features in edge devices, before they become commercially available, we provision for Model Privacy and a User-Centred IoT ecosystem, where service providers can have trust in the authenticity, attestability, and trustworthiness of the valuable models running on user devices, without the users having to reveal sensitive personal information to these cloud-based centralised systems. This approach will enable advanced and sensitive edge-based analytics to be performed, without jeopardising the individuals' privacy. Importantly, we aim to integrate mechanisms for data authenticity and attestation into our proposed framework, to enable trust in models and the data used by them. Such privacy-preserving technologies have the capacity to enable new form of sensitive analytics, without sharing raw data and thereby providing legal balancing capabilities that might enable certain sensitive (or currently unlawful) data analysis.

Internet eXchange Points (IXPs) have become a critical element of the Internet, as they provide the physical locations where networks interconnect and exchange traffic. IXPs carry huge traffic volumes, reduce interconnection costs, and hence make national Internet access affordable. Despite the growth of these infrastructures, the rapid evolution of the Internet poses new challenges. Reacting as soon as possible to the highly dynamic Internet environment has always been the first priority for Network Operators. Unfortunately, state-of-the-art techniques are extremely limited. Networks use the Border Gateway Protocol (BGP) to inform each other of which destinations are reachable. Accordingly, network operators (ab)use BGP Traffic Engineering (TE) to tweak traffic paths. TE is a network-management tool allowing a network to adapt events ranging from a change in customer location to mitigating dramatically large traffic outbursts of a malicious Distributed Denial of Service (DDoS) attack. However, BGP-TE lacks programmability and dynamism: once BGP preferences are set up, they cannot react in real-time to network events. With a high-fidelity measurement-focused approach, a network could implement more sophisticated traffic management techniques. For example, any network connected through an IXP must implement ingress traffic filtering to avoid receiving undesirable traffic (e.g., DDoS attacks or resulting from misconfigurations). However, correctly controlling ingress filters is complex. Thus, most IXP customers unrealistically expect the organisations originating the traffic to manage any problem. TE limitations result from the inability of current Internet monitoring techniques to cope with the wide range of granularities of network events. While control plane related events (those concerned with the selection of paths/routes, such as BGP updates) happen at a time scale of minutes, data plane events (packet processing) occur at time-scales of micro-seconds. While control plane monitoring is relatively easy, data plane observability is poor, relies on expensive equipment, and does not scale. EARL addresses this imbalance between the ability to observe control and data plane, and the consequent limits on the detection and reaction to network events. EARL is a novel integration of monitoring mechanisms and reactive network management. EARL enables a prompt reaction to network events with its Software Defined Networking (SDN) approach. Because of the IXP's central role on the Internet and the critical nature at the national level, we believe that they are the ideal place to explore EARL's ideas. We will demonstrate how measurement-assisted network management permits new Internet-wide services and, enables the provision of services hitherto considered impossible or too costly to deploy. Our goal for the EARL project is to pioneer SDN enabled measurement-based network management to enhance the Internet infrastructure. This will lead to relevant tools and data for the larger researcher and practitioner communities. To this aim, we will create a new research instrument, EARLnet: an operational, research-centered, Autonomous System (AS) directly connected to our partners, providing a new and unique real-world environment for the real-time monitoring of network status and SDN-oriented research. EARLnet will serve also as a test-bed to develop and evaluate novel reactive network management solutions. The EARL project has the potential to revolutionise current Internet network management through new fine-grained and reactive TE policies. EARL will not only create new mechanisms, but also translate the blind, legacy BGP-based, TE into measurement-assisted SDN techniques. Furthermore, through our partner institution, the Cambridge Cloud Cybercrime Centre (CCCC), EARLnet will provide valuable data to a large community of researchers and practitioners.