Programming is intrinsically based on the use of limited resources, such as memory and processing power of computers. Various abstractions of resources play an important role throughout computer science, but they are conceptualised in very different, and apparently unrelated ways. In particular, there is a big gap between studies focussing on precise quantitative issues of what we can do and how efficiently we can do it with limited resources, and those which concern more conceptual aspects, which underpin modern high-level programming languages, and application-oriented programming. In this project, building on some recent breakthrough developments which relate these different aspects, we aim to develop a unified theory of resources which will apply to all these aspects, and allow a flow of ideas between them. This will provide new tools and methods for computer scientists, and lead both to new kinds of results, and more general versions of existing ones.

The assessment of muscle activity has become an essential indicator in medical diagnosis, motor rehabilitation, health monitoring, and neuroprosthetic/robotic control. Recent technological advances allow diseases that affect muscles and peripheral nerves to be recorded and diagnosed remotely and continuously. Motivated by exploring the electrophysiological behaviour of the uterus before childbirth, magnetomyography (MMG) was used for health monitoring during pregnancy. In addition, MMG can be used to rehabilitate, for example, traumatic nerve injuries, spinal cord lesions, and entrapment syndrome. SUPREMISE is an ambitious, speculative, interdisciplinary, and creative fellowship programme of research that has the potential to address unmet clinical, leading to radically new technologies for muscle movement recording, creating a paradigm shift in neuromuscular patients and beyond e.g. human-machine interfacing for extended reality, gaming, and consumer electronics. The discoveries, research and new knowledge created within this fellowship will lead to a world-leading research group that will position the UK at the forefront of this emergent field. SUPREMISE will create the first wearable spintronic sensor for measuring MMG signals in the clinical setting. SUPREMISE will involve radical innovations in magnetic sensors, microelectronics, wearable devices, muscle neuroscience, and signal processing. A principal aim is to make a transformative impact on the lives of patients affected by neuromuscular diseases by developing novel sensing diagnosis wearables based on spintronics that record and measure muscle activity. A paradigm-shifting engineering technology will be proposed by interfacing cutting-edge theoretical, computational, and experimental physics with advanced biomedical modelling and testing. While muscle activity which is linked to neuromuscular diseases, has captured the attention of the healthcare community, the magnetic recording approach to diagnosis has not been systematically applied through a robust and reliable tool. SUPREMISE will standardize the efficient utilization of the MMG sensor to detect such muscle activity for clinical deployment. Miniaturizing magnetic sensing systems offer the prospect of replacing bulky laboratory instruments with easy-to-use wearable clinical platforms. It would decrease the cost (< £5), size, and noise floor by several orders of magnitude. Here, we propose a novel solution using nanofabricated spintronic TMR-based sensors integrated with the ASIC readout interface. This new wearable system with a small footprint, excellent sensitivity, ultralow noise, and excellent spatial resolution can detect low pico-Tesla (pT) magnetic fields generated by the muscle. Given my published and peer-reviewed pilot research, I believe that we are at the stage where a combination of modelling and experimental work will accelerate progress. The project's results will target the development of a new miniaturized platform for muscle assays that refines the measurement of the MMG signals and streamlines techniques for use by clinicians.

Chromonics are a fascinating class of lyotropic liquid crystals. They are usually formed in water from plate-like molecules, which self-assemble into aggregate stacks (rods or layers), which in turn self-organise to form liquid crystals. Chromonics are very poorly understood. Researchers are just beginning to understand how self-assembly is influenced by the interactions between molecules and how the process can be controlled by use of additives (such as small molecules or salt). Moreover, many known chromonic materials are based on industrial dyes, which are very difficult to purify; and this hampered some of the early investigations into phases and phase behaviour. Despite these difficulties it is beginning to be recognised that chromonic systems are far more common than once thought. Formation of stacked aggregates in dilute solution and/or chromonic mesophases at higher concentrations, have been widely reported in aqueous dispersions of many formulated products such as pharmaceuticals and dyes used in inkjet printing. Recently, there has been greatly enhanced interest in chromonics materials as functional materials for fabricating highly ordered thin films, as biosensors, and chromonic stacks have also been used to aid in the controllable self-assembly of gold nanorods. This proposal seeks to develop a novel class of chromonic molecules: nonionic chromonics based on ethylenoxy groups. Here, we will design new chromonic phases demonstrating novel structures (such as hollow water-filled columns and layered brick-like phases), which can be used for future applications. We will also investigate and control the self-assembly process, in a class of materials that can be purified, that are not influenced as strongly by salt (compared to most industrial dyes), where structural changes can be easily engineered by minor changes to a synthetic scheme, and where addition of other solvents can lead to major changes in both self assembly and phase behaviour. We will also use state-of-the-art modelling and theory, which has recently been shown to provide new insights into self-assembly in chromonics, to help design new materials. Here, the use of quantitative and semi-quantitative molecular modelling provides for the possibility of "molecular engineering" new phases. To accomplish our goals for this project we will bring together synthetic organic chemistry to design and make new materials; state-of-the-art physical organic measurements to characterise both the nature of self-assembly and the novel chromonic phases formed; and state-of-the-art modelling/theory to predict, explain and help control the chromonic aggregation.

In today's society, the massive deployment of smart devices, the popularity of online services and social networks, and the increasing global data traffic, makes the ability to process large data volumes absolutely crucial. Demand for Artificial Intelligence (AI) has therefore exploded, fuelled by an increasing number of industries (e.g. energy, finance, healthcare, defence) heavily relying on the efficient processing of large data sets. Nonetheless, the ever-growing data processing demand creates a pressing need to find new paradigms in AI going beyond current systems, capable of operating at very high speeds whilst retaining low energy consumption. The human brain is exceptional at performing very quickly, and efficiently, highly complex computing tasks such as recognising patterns, faces in images or a specific song from just a few sounds. As a result, computing approaches inspired by the powerful capabilities of networks of neurons in the brain are the subject of increasing research interest world-wide, and are in fact already used by current AI platforms to perform these (and other) complex functions. Whilst these brain-inspired artificial neural networks (ANNs) are supported to date by traditional micro-electronic technologies, photonic techniques for brain emulation have also recently started to emerge due to their unique and superior properties. These include very high speeds and reduced interference, among others. Remarkably, ubiquitous photonic devices such as vertical-cavity surface emitting lasers (VCSELs), the very same devices used in supermarket barcode scanners, computer mice and in mobile phones for auto-focus functionalities, can exhibit responses analogous to those of neurons but up to 1 billion times faster. VCSELs are also compact, inexpensive and allow practical routes for integration in chip modules with very low footprints (just a few mm2) making them ideal for the development of ultrafast photonic ANNs using ultrafast light signals instead of electric currents to operate. This permits exploring radically new research directions aiming at exploiting the full potential of light-enabled technologies for new paradigms in ultrafast AI. This Fellowship project will focus on this key challenge to develop transformative photonic ANNs using VCSELs as building blocks capable of performing complex computational tasks at ultrafast speeds, using data rates below 1 billionth of a second to operate. These will include the ultrafast prediction of complex data signals, of interest for example in meteorology forecasting, to very high speed data classification of interest in green-energy systems (e.g. analysis of wind patterns in off-shore wind-energy farms). The research milestones of this programme are: (1) the design and fabrication of photonic ANNs using coupled VCSELs as building blocks, emulating the operation of the human brain at ultrafast speeds; (2) the development of chip-scale modules of VCSEL based photonic ANNs; (3) the demonstration of complex data processing tasks with photonic ANNs at ultrafast speeds (at data rates below 1 billionth of a second); (4) the delivery of photonic systems for AI, tackling key functionalities across strategic UK economic sectors (e.g. energy, defence). In summary, by bringing together the hitherto disparate fields of brain-inspired computing and photonics, this programme proposes unique pioneering research in photonic ANNs for future ultrafast AI technologies.