An enormous amount of individuals' data is collected every day. These data could potentially be very valuable for scientific and medical research or for targeting business. Unfortunately, privacy concerns restrict the way this huge amount of information can be used and released. Several techniques have been proposed with the aim of making the data anonymous. These techniques however lose their effectiveness when attackers can exploit additional knowledge. Differential privacy is a promising approach to the privacy-preserving release of data: it offers a strong guaranteed bound on the increase in harm that a user I incurs as a result of participating in a differentially private data analysis, even under worst-case assumptions. A standard way to ensure differential privacy is by adding some statistical noise to the result of a data analysis. Differentially private mechanisms have been proposed for a wide range of interesting problems like statistical analysis, combinatorial optimization, machine learning, distributed computations, etc. Moreover, several programming language verification tools have been proposed with the goal of assisting a programmer in checking whether a given program is differentially private or not. These tools have been proved successful in checking differentially private programs that uses standard mechanisms. They offer however only a limited support for reasoning about differential privacy when this is obtained using non-standard mechanisms. One limitation comes from the simplified probabilistic models that are built-in to those tools. In particular, these simplified models provide no support (or only very limited support) for reasoning about explicit conditional distributions and probabilistic inference. From the verification point of view, dealing with explicit conditional distributions is difficult because it requires finding a manageable representation, in the internal logic of the verification tool, of events and probability measures. Moreover, it requires a set of primitives to handle them efficiently. In this project we aim at overcoming these limitations by extending the scope of verification tools for differential privacy to support explicit reasoning about conditional distributions and probabilistic inference. Support for conditional distributions and probabilistic inference is crucial for reasoning about machine learning algorithms. Those are essential tools for achieving efficient and accurate data analysis for massive collection of data. So, the goal of the project is to provide a novel programming language technology useful for enhancing privacy-preserving data analysis based on machine learning.

Addressing the health needs of a growing and ageing population is a central challenge facing modern society. Technology is enabling the collection of increasingly large and heterogeneous biomedical data sets, yet interpreting such data to gain knowledge about disease mechanisms and clinical and preventative strategies is still a major open problem. Artificial Intelligence (AI) techniques hold huge promise to provide an integrative framework for extracting knowledge from data, with a high potential for fundamental and clinical breakthroughs with significant impact both on public health and on the future of the UK bioeconomy. The ambition of the proposed CDT is to train a cadre of highly skilled interdisciplinary scientists who will spearhead the development and deployment of AI techniques in the biomedical sector. Achieving our long-term aims will require several hurdles to be overcome. The biomedical sector poses unique methodological challenges to AI technology, due to the need of interpretable models which can quantify uncertainties within predictions. It also presents formidable cultural and technical language barriers, requiring honed communication skills to overcome disciplinary boundaries. Perhaps most importantly, it requires researchers and practitioners with a keen awareness of the societal, legal and ethical dimension of their research, who are able to reach out to societal stakeholders, and to anticipate and engage with the potential issues arising from deploying AI technology in the biomedical sector. We will realise our ambition through a structured training programme: students will initially acquire the foundational skills in a Master by Research first year, which includes taught courses on the technical, biomedical and socio-ethical aspects of biomedical AI, and provides multiple opportunities to directly experience interdisciplinary research through rotation projects. Students will then acquire in depth research experience through an interdisciplinary PhD, bridging between the University of Edinburgh's world-leading institutions pursuing informatics and biomedical research. Students will benefit from a large and exceptionally distinguished faculty of potential supervisors: over 60 academics including several fellows of the Royal Society/ Royal Society of Edinburgh, and over forty recipients of prestigious fellowships from the ERC, the research councils, and biomedical charities such as the Wellcome Trust. This training programme will be interleaved with intensive training in interdisciplinary communication and science communication, and will offer multiple opportunities to engage with external stakeholders including industrial and NHS internships.

The digital games industry has global revenues of $65bn (in 2011) predicted to grow to $82bn by 2017. The UK is a major player, whose position at third internationally (behind the US and Japan) is under threat from China, South Korea and Canada. The £3bn UK market for games far exceeds DVD and movie box office receipts and music sales. Driven by technology advances, the industry has to reinvent itself every five years with the advent of new software, interaction and device technologies. The influential 2011 Nesta "Next Gen" review of the skills needs of the UK Games and Visual Effects industry found that more than half (58%) of video games employers report difficulties in filling positions with recruits direct from education and recommended a substantial strengthening of games industry-university research collaboration. IGGI will create a sustainable centre which will provide the ideal mechanism to consolidate the scientific, technical, social, cultural and cognitive dimensions of gaming, ensuring that the industry benefits from a cohort of exceptional research-trained postgraduates and harnessing research-led innovation to ensure that the UK remains at the forefront of innovation in digital games. The injection of 55+ highly qualified PhD graduates and their associated research projects will transform the way the games industry works with the academic community in the UK. IGGI will provide students with a deep grounding in the core technical and creative skills needed to design, develop and deliver a game, as well as training in the scientific, social, therapeutic and cultural possibilities offered by the study of games and games players. Throughout their PhDs the students will participate in practical industrial workshops, intensive game development challenges and a yearly industrialy-facing symposium. All students will undertake short- and longer-term placements with companies that develop and use games. These graduates will push the frontiers of research in interaction, media, artificial intelligence (AI) and computational creativity, creating new game-themed research areas at the boundaries of computer science and economics, sociology, biology, education, robotics and other fields. The two core themes of IGGI are: Intelligent Games - increasing the flow of intelligence from research into digital games. We will use research advances to seed the creation of a new generation of more intelligent and engaging digital games, to underpin the distinctiveness and growth of the UK games industry. The study of intelligent games will be underpinned by new business models and research advances in data mining (game analytics) which can exploit vast volumes of gameplay data. Game Intelligence - increasing the use of intelligence from games to achieve scientific and social goals. Analysis of gameplay data will allow us to understand individual behaviour and preference on a hitherto impossible scale, making games into a powerful new tool to achieve scientific and societal goals. We will work with user groups and the games industry to produce new genres of games which can yield therapeutic, educational and social benefits and use games to seed a new era of scientific experimentation into human behaviour, preference and interaction, in economics, sociology, psychology and human-computer-interaction. The IGGI CDT will provide a major advance in an area of great importance to the UK economy and massive impact on society. It will provide training for the leaders of the next generation of researchers, developers and entrepreneurs in digital games, forging economic growth through a distinctly innovative and research-engaged UK games industry. IGGI will massively boost the notion of digital games as a tool for scientific research and societal good.