When your PC power is cut off, or your mobile phone runs out of battery, anything you were working on at the time (e.g. a game of Solitaire) is lost forever. This is because the RAM, the memory your device uses to store data temporarily, is wiped clean whenever it loses power. However, documents you saved on your PC or the photos you took on your phone are still on your device when it switches back on. This is because they are saved to the hard drive, which is permanent storage that keeps its data even if it loses power. Recently, technology companies have manufactured a new type of RAM, non-volatile memory or NVM, that does not lose its data when it loses power. This means that when a device switches back on after losing power or something catastrophic like a crash, its data is still available on NVM; we say that its data persists. This means that with careful engineering we may recover the data and not lose our work. However, this is not quite so straightforward. Modern devices are very fast, and to do this they use clever methods to get work done efficiently. For example, they do multiple tasks all at once or do a list of tasks in a different order. Sometimes this means leaving your data in a bad state temporarily and fixing it later, e.g. deleting your old data before saving a new version. If we lose power during a bad temporary state (e.g. after deleting old data but before saving its replacement), then when we restart the device we may recover bad data. This has many unfortunate consequences, from simply losing data to causing errors in our software. My research project will solve these problems, by studying NVM use from the perspective of hardware (e.g. our phones), software (e.g. our phone apps), and theoretical analysis. I will develop new tools and techniques that will help us build persistent technology, and then use formal (mathematical) methods to prove that these tools and techniques are safe and correct. My proposed research has three key components. First, I will create NVM 'persistency models', which are rigorous ways of describing exactly what NVM can/cannot do, with mathematical precision. I will then use specialised tools to test Intel and ARM microchips (in our PCs and phones) against my models, and see how they behave when using NVM. Verifying that real-world hardware behaves as expected is an important step towards safe and reliable NVM, as it provides a safe foundation to write software on top of. Second, I will extend modern programming languages to enable writing programs (software) that can control how data persists to NVM, which in turn makes it easier and safer to recover NVM data. Currently it is impossible to write such programs, because NVM is such a new concept that persistence control is not a part of modern programming languages. I will extend these languages and provide example programs and tests. I will then prove that these extensions are correct so that software companies can rely on them to build their future products. Finally, I will develop ways to test and verify that programs safely recover NVM data. Testing is an important part of hardware and software development, but testing NVM persistency is currently infeasible: the only way to do this currently is to run thousands of tests, each time cutting the power at different times. However, forcing such frequent power losses is both impractical and inefficient. I will develop new ways to test NVM persistency, which is the final key step for widespread NVM adoption. NVM could save untold amounts of data, money and time every year. Data loss is faced by not only people who use computers every day, but also data centres and safety-critical technologies worldwide. NVM can make data loss a thing of the past, but requires a rigorous, safe foundation to be built on, to avoid trading one kind of unpredictability for another. This research project will ensure that foundation, and unleash the potential of this new technology.

Computational simulations allow us to make predictions about how biological molecules interact with (stick to) each other, and how these interactions, if they go wrong, can lead to disease. If this is understood then there is the potential to design new drugs that prevent this unwanted interaction between the protein molecules, and so treat the disease. This approach has great potential in areas as diverse as cancer therapy and new antibiotics. The problem is that the computer simulations needed for this type of study are enormous - typically they require access to the world's largest supercomputers. However, new research has shown how the same type of simulation study can be accomplished by spreading the work over very large numbers of smaller computers which may be spread all around the world. Such computer facilities - "the cloud" - are already incredibly important in fields ranging from business to social media, but the idea hasn't yet really made an impact in computational medical science. Our aim is to help this happen. Building on years of previous experience developing computer software to help biological scientists and chemists easily use supercomputers for their research, we will develop a toolkit for "cloud-based computational chemistry". This will make it possible for far more researchers, all round the world, to do the same sort of cutting-edge medical research that until now was only possible for those groups who could access a supercomputer. We will test the power of this new facility by using it to study two particular diseases - cancer and antibiotic resistance. In both cases we will build on the research experience and interests of our industrial partner, the pharmaceutical company UCB Celltech. This ensures that, should we get some promising results, the theoretical predictions can quickly be tested in the lab, and if they hold up, taken forward into the development of new drugs for these key health problems.

The ecosystem of compute devices is highly connected, and likely to become even more so as the internet-of-things concept is realized. There is a single underlying global protocol for communication which enables all connected devices to interact, i.e. internet protocol (IP). In this project, we will create a corresponding single underlying global protocol for computation. This will enable wireless sensors, smartphones, laptops, servers and cloud data centres to co-operate on what is conceptually a single task, i.e. an AnyScale app. A user might run an AnyScale app on her smartphone, then when the battery is running low, or wireless connectivity becomes available, the app may shift its computation to a cloud server automatically. This kind of runtime decision making and taking is made possible by the AnyScale framework, which uses a cost/benefit model and machine learning techniques to drive its behaviour. When the app is running on the phone, it cannot do very complex calculations or use too much memory. However in a powerful server, the computations can be much larger and complicated. The AnyScale app will behave in an appropriate way based on where it is running. In this project, we will create the tools, techniques and technology to enable software developers to create and deploy AnyScale apps. Our first case study will be to design a movement controller app, that allows a biped robot with realistic humanoid limbs to 'walk' over various kinds of terrain. This is a complex computational task - generally beyond the power of embedded chips inside robotic limbs. Our AnyScale controller will offload computation to computers on-board the robot, or wirelessly to nearby servers or cloud-based systems. This is an ideal scenario for robotic exploration, e.g. of nuclear disaster sites.

Progress in sensing, computational power, storage and analytic tools has given us access to enormous amounts of complex data, which can inform us of better ways to manage our cities, run our companies or develop new medicines. However, the 'elephant in the room' is that when we act on that data we change the world, potentially invalidating the older data. Similarly, when monitoring living cities or companies, we are not able to run clean experiments on them - we get data which is affected by the way they are run today, which limits our ability to model these complex systems. We need ways to run ongoing experiments on such complex systems. We also need to support human interactions with large and complex data sets. In this project we will look at the overlap between the challenge someone faces when coping with all the choices associated with booking a flight for a weekend away, and an expert running complex experiments in a laboratory. The project will test the core ideas in a number of areas, including personalisation of hearing aids, analysis of cancer data, and adapting the computing resources for a major bank.

Society is seeing enormous growth in the development and implementation of autonomous systems, which can offer significant benefits to citizens, communities, and businesses. The potential for improvements in societal wellbeing is substantial. However, this positive potential is balanced by a similar potential for societal harm through contingent effects such as the environmental footprint of autonomous systems, systemic disadvantage for some socio-economic groups, and entrenchment of digital divides. The rollout of autonomous systems must therefore be addressed with responsibilities to society in mind. This must include engaging in dialogue with society and with those affected, trying to anticipate challenges before they occur, and responding to them. One such anticipated challenge is the effect of change on autonomous systems. Autonomous systems are not designed to be deployed in conditions of perfect stasis, as they are unlikely to encounter such conditions in real-world environments. They are frequently designed for changing environments, like public roads, and may also be designed to change themselves over time, for instance by means of learning capabilities. Not only that, but these changes in deployed systems and in their operating conditions are also likely to take place against a shifting contextual background of societal alteration (e.g. other technologies, 'black swan' events, or simply the day-to-day operation of communities). The effects of such change, on the systems themselves, on the environments within which they are operating, and on the humans with which they engage, must be considered as part of a responsible innovation approach. The RAILS project brings together a team from UCL and the Universities of York, Leeds and Oxford, from multiple disciplines, with the aim of engaging with the challenges associated with the long-term operation of autonomous systems and the effects of change on these systems. In particular, we will explore how the notion of responsibility is affected by (i) open-ended dynamic environments - situations that change over time, and (ii) lifelong-learning systems - i.e. systems that are designed to adapt themselves to their circumstances and 'learn' over time. The RAILS project will focus on such independent long-term autonomous systems in different applications. These will include (i) autonomous vehicles and (ii) autonomous robot systems such as unmanned aerial vehicles (drones). RAILS will look at social and legal contexts, as well as technical requirements, in order to assess whether and how these systems can be designed, developed, and operated in a way that they are responsible, accountable, and trustworthy. The overall aim of the RAILS project is to bring together responsible development principles with governance mechanisms and technical understanding to create new understandings of how autonomous systems can adapt to change, how they can be deployed in a responsible and trustworthy way, and how such deployment can be framed by governance to ensure accountability and flexibility.