CausaLens
CausaLens
2 Projects, page 1 of 1
assignment_turned_in Project2024 - 2029Partners:CausaLens, Healthcare Improvement Scotland, Scottish Ambulance Service, Amazon (United States), QMUL +44 partnersCausaLens,Healthcare Improvement Scotland,Scottish Ambulance Service,Amazon (United States),QMUL,The Data Lab,ARCHIMEDES,Zeit Medical,Chief Scientist Office (CSO), Scotland,Sibel Health,Facebook (United States),Microsoft Research (United Kingdom),Gendius Limited,University of Dundee,CANCER RESEARCH UK,Health Data Research UK,UCB Pharma UK,Institute of Cancer Research,University of California Berkeley,Spectra Analytics,McGill University,Life Sciences Scotland,Indiana University Bloomington,Kheiron Medical Technologies,Scottish AI Alliance,Willows Health,Nat Inst for Health & Care Excel (NICE),Canon Medical Research Europe Ltd,Scotland 5G Centre,Mayo Clinic,Evergreen Life,The MathWorks Inc,NHS Lothian,NHS GREATER GLASGOW AND CLYDE,PrecisionLife Ltd,Huawei Technologies R&D (UK) Ltd,Bering Limited,Research Data Scotland,ELLIS,Manchester Cancer Research Centre,Endeavour Health Charitable Trust,Digital Health & Care Innovation Centre,Hurdle,British Standards Institution,University of Edinburgh,Data Science for Health Equity,NHS NATIONAL SERVICES SCOTLAND,Samsung AI Centre (SAIC),Univ Coll London Hospital (replace)Funder: UK Research and Innovation Project Code: EP/Y028856/1Funder Contribution: 10,288,800 GBPThe current AI paradigm at best reveals correlations between model input and output variables. This falls short of addressing health and healthcare challenges where knowing the causal relationship between interventions and outcomes is necessary and desirable. In addition, biases and vulnerability in AI systems arise, as models may pick up unwanted, spurious correlations from historic data, resulting in the widening of already existing health inequalities. Causal AI is the key to unlock robust, responsible and trustworthy AI and transform challenging tasks such as early prediction, diagnosis and prevention of disease. The Causality in Healthcare AI with Real Data (CHAI) Hub will bring together academia, industry, healthcare, and policy stakeholders to co-create the next-generation of world-leading artificial intelligence solutions that can predict outcomes of interventions and help choose personalised treatments, thus transforming health and healthcare. The CHAI Hub will develop novel methods to identify and account for causal relationships in complex data. The Hub will be built by the community for the community, amassing experts and stakeholders from across the UK to 1) push the boundaries of AI innovation; 2) develop cutting-edge solutions that drive desperately needed efficiency in resource-constrained healthcare systems; and 3) cement the UK's standing as a next-gen AI superpower. The data complexity in heterogeneous and distributed environments such as healthcare exacerbates the risks of bias and vulnerability and introduces additional challenges that must be addressed. Modern clinical investigations need to mix structured and unstructured data sources (e.g. patient health records, and medical imaging exams) which current AI cannot integrate effectively. These gaps in current AI technology must be addressed in order to develop algorithms that can help to better understand disease mechanisms, predict outcomes and estimate the effects of treatments. This is important if we want to ensure the safe and responsible use of AI in personalised decision making. Causal AI has the potential to unearth novel insights from observational data, formalise treatment effects, assess outcome likelihood, and estimate 'what-if' scenarios. Incorporating causal principles is critical for delivering on the National AI Strategy to ensure that AI is technically and clinically safe, transparent, fair and explainable. The CHAI Hub will be formed by a founding consortium of powerhouses in AI, healthcare, and data science throughout the UK in a hub-spoke model with geographic reach and diversity. The hub will be based in Edinburgh's Bayes Centre (leveraging world-class expertise in AI, data-driven innovation in health applications, a robust health data ecosystem, entrepreneurship, and translation). Regional spokes will be in Manchester (expertise in both methods and translation of AI through the Institute for Data Science and AI, and Pankhurst Institute), London (hosted at KCL, representing also UCL and Imperial, leveraging London's rapidly growing AI ecosystem) and Exeter (leveraging strengths in philosophy of causal inference and ethics of AI). The hub will develop a UK-wide multidisciplinary network for causal AI. Through extended collaborations with industry, policymakers and other stakeholders, we will expand the hub to deliver next-gen causal AI where it is needed most. We will work together to co-create, moving beyond co-ideation and co-design, to co-implementation, and co-evaluation where appropriate to ensure fit-for-purpose solutions Our programme will be flexible, will embed trusted, responsible innovation and environmental sustainability considerations, will ensure that equality diversity and inclusion principles are reflected through all activities, and will ensure that knowledge generated through CHAI will continue to have real-world impact beyond the initial 60 months.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2024 - 2032Partners:In2science UK, Queensland University of Technology, Imperial College London, Ecole Polytechnique, University of Minnesota +72 partnersIn2science UK,Queensland University of Technology,Imperial College London,Ecole Polytechnique,University of Minnesota,Harvard University,AIMS,Free (VU) University of Amsterdam,Aarhus University,Microsoft (United States),The University of Texas MD Anderson Cancer Center,Spectra Analytics,ELEMENTAL POWER LTD,British Broadcasting Corporation - BBC,Meta,IBM Research,University of Padua (Padova),UCD,Kaiju Capital Management Limited,Sandia National Laboratories California,Simon Fraser University,CausaLens,dunnhumby Limited,Swiss Federal Inst of Technology (EPFL),BASF SE,King Abdullah University of Science and Technology,University of Western Australia,Atomic Weapons Establishment,American Express,UNIBO,LUISS Guido Carli University,Columbia University,Rakai Health Sciences Program,Novo Nordisk (Denmark),Shell International Petroleum CompanyLtd,Duke University,GSK,Institute of Tropical Medicine,Los Alamos National Laboratory,MediaTek,University of California Davis,Pennsylvania State University,J.P. Morgan,3C Capital Partners,Spotify UK,ASOS Plc,Securonix,JAGUAR LAND ROVER LIMITED,Arctic Wolf Networks,McGill University,Martingale Foundation,Australian National University,Monash University,Criteo Technology,University of Melbourne,Cancer Research UK Convergence Science,Leibniz Institute for Prevention Researc,PANGEA-HIV consortium,NewDay Cards Ltd,Korea Advanced Institute of Science and Technology,Stanford University,Optima Partners,OFFICE FOR NATIONAL STATISTICS,Paris Dauphine University - PSL,CCFE/UKAEA,ETH Zurich,Deutsche Bank (United Kingdom),Addionics Limited,UofT,University of Chicago,Università Luigi Bocconi,Johns Hopkins University,Novartis Pharmaceutical Corporation,Qube Research & Technologies,Alpine Intuition Sarl,G-Research,Centre National de la Recherche Scient.Funder: UK Research and Innovation Project Code: EP/Y034813/1Funder Contribution: 7,873,680 GBPThe EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) will address the EPSRC research priority of the 'physical and mathematical sciences powerhouse' through an innovative cohort-based training program. StatML harnesses the combined strengths of Imperial and Oxford, two world-leading institutions in statistics and machine learning, in collaboration with a broad spectrum of industry partners, to nurture the next generation of leaders in this field. Our students will be at the forefront of advancing the core methodologies of data science and AI, crucial for unlocking the value inherent in data to benefit industry and society. They will be equipped with advanced research, technical, and practical skills, enabling them to make tangible real-world impacts. Our students will be ethical and responsible innovators, championing reproducible research and open science. Collaborating with students, charities and equality experts, StatML will also pioneer a comprehensive strategy to promote inclusivity, attract individuals from diverse backgrounds and eliminate biases. This will help diversify the UK's future statistics and machine learning workforce, essential for ensuring data science is used for public good. Data science and AI are now part of our everyday lives, transforming all sectors of the economy. To future-proof the UK's prosperity and security, it is essential to develop new methodology, specifically tailored to meet the big societal challenges of the future. The techniques underpinning such methods are founded in statistics and machine learning. Through close collaboration with a broad range of industry partners, our cohort-based training will support the UK in producing a critical mass of world-leading researchers with expertise in developing cutting-edge, impactful statistical and machine learning methodology and theory. It is well documented in government and learned society reports that the UK economy has an urgent need for these people. The significant level of industry support for our proposal also highlights the necessity of filling this gap in the UK data science ecosystem. StatML will learn from and build upon our previous successful experiences in cohort training of doctoral students (our existing StatML CDT funded in 2018, as well as other CDTs at Imperial and Oxford). Our students will continue to produce impactful, internationally leading research in statistics and machine learning (as evidenced by our students' impressive publication record and our world-leading research environment, as rated by the REF 2021 evaluation), while complementing this with a bespoke cohort-based Advanced Training program in Statistics and Machine Learning (StatML-AT). StatML-AT has been developed from our experience and in partnership with industry. It will be responsive to emerging technologies and equip our students with the practical skills required to transform how data is used. It will be delivered by our outstanding academics from both institutions alongside with industry leaders to ensure that students receive training in cutting edge technologies, along with the latest ideas in ethics, responsible innovation, sustainability and entrepreneurship. This will be complemented by industrial and academic placements to allow the students to develop their own international network and produce high-impact research. Together, StatML and its partners will train 90+ students over 5 cohorts. More than half of these will be funded from external sources, including 25+ by industry, representing excellent value for money. Our diverse cohorts will benefit from a unique and responsive training program combining academic excellence, industry engagement, and interdisciplinary culture. This will make StatML a vibrant research environment inspiring the next methodological advancements to transform the use of data and AI across industry and society.
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