Severe mental illness dramatically impacts the wellbeing of affected individuals and their families and are a leading cause of disability worldwide. In the UK alone, around 13.5 million people are affected by mental illness each year, which places a significant burden on the economy through direct (health care) and indirect (lost work and productivity) costs. Current treatments for severe mental illness, such as schizophrenia, have limited efficacy. To improve patient outcomes, there is an urgent need to develop more effective treatments in psychiatry, as well as approaches to target treatments to individuals who are most likely to benefit from them. While the exact causes of severe mental illness are unknown, a wide body of research has demonstrated an important role for genetic factors. For example, common and rare genetic variants spread across many different genes have been identified as risk factors for schizophrenia. However, no individual genetic cause is by itself necessary or sufficient for the development of schizophrenia. Moreover, different combinations of risk factors are observed in individuals with the same psychiatric diagnosis, and some genetic factors are shared across different mental health conditions. This complexity has made it challenging to translate genomic discoveries in conditions such as schizophrenia to improvements in patient care. During the renewal phase of my Fellowship, I will leverage new genomic resources to enhance our understanding of the genetic basis of severe mental illness. I will focus on two key research areas that build directly on findings from the first phase of my Fellowship. The first aims to identify genetic links to specific clinical features and treatment outcomes in individuals diagnosed with schizophrenia, bipolar disorder or severe depression. This research is needed because symptoms vary widely among individuals with a particular mental illness, and individuals with the same disorder do not always respond to the same treatments. By understanding how genetic factors contribute to this variation, we can uncover the biological causes of these symptoms, which in turn, may lead to the development of more effective treatments. Moreover, discovering genetic factors that influence treatment outcomes can inform approaches aiming to identify patient groups more likely to benefit from specific treatments. Finally, I will examine whether genetic variants that are already known to increase risk for schizophrenia have effects on other medical outcomes, such as diabetes, hypertension or renal failure, which will help clinicians in planning and managing the care of people with these mutations. The second area of research aims to address gaps in our understanding about the utility of DNA sequencing for detecting clinically relevant mutations in individuals with severe mental illness. Confirming a genetic contribution to a patient's condition can be beneficial, particularly in terms of genetic counselling. To better understand the role of genetic testing in mental illness, it is important to determine the proportion of patients carrying a clinically relevant mutation. I will conduct the largest sequencing study to date examining the rates of clinically relevant mutations in severe mental illness. I will also investigate whether individuals with a severe mental illness and a co-occurring medical condition, like developmental delay, are more likely to carry clinically relevant mutations. This information will help identify which patient groups should be prioritised for genetic testing. The long-term goal of my Fellowship is to advance precision medicine in psychiatry. I will work with selected partners from industry and the NHS to accelerate this process and ensure that my findings contribute to improvements in clinical care for individuals with severe mental illness.

We generate vast amounts of data concerning our health, movements, and habits when interacting with technology and digital services. These digital traces are a vital key to solving society's biggest problems-for example, electronic health records can support cancer surveillance efforts, and mobile location data can support humanitarian action for disaster relief. While privacy researchers have proposed numerous techniques to safely collect, analyse, and share personal data, these systems are not without their limits. Indeed, a number of supposedly anonymous datasets have been re-identified, and a lack of public confidence derailed the NHS's care.data and GP data collection scheme that tried to share de-identified health data for research. To address the privacy threats involved in releasing sensitive human data, regulators have advocated for use of modern privacy-enhancing technologies (PETs) that have stronger privacy guarantees. However, some PET techniques-such as injecting noise into the data, or creating 'synthetic' datasets-can fundamentally distort data in unknown but potentially harmful ways, for example if rare diseases are suppressed from synthetic data, or vulnerable communities are further marginalised. A group of 50 US academics led by Prof. Gary King recently warned the US Census Bureau that the secrecy of anonymisation techniques can lead to "biases that have never been publicly quantified". This lack of understanding of how PETs will impact research and data analysis-and the policy interventions that rely on it-complicates recent calls to "unlock the power of data" for the public good. Over the course of this Fellowship, I will provide a pathway to guarantee both the privacy of data subjects *and* the utility and integrity of research data. My proposal pioneers a statistical learning and computational approach to guide the development of fair and usable PETs, allowing regulators and civil society-for the first time-to make evidence-based determinations for which privacy mechanisms to use when collecting and releasing sensitive datasets, and researchers to independently audit the validity and integrity of any anonymised data they receive. It will pioneer computationally-heavy replication studies to understand how PETs can cause harm (WP1); statistical methods to help PET developers 'extrapolate' guarantees from lab studies to the real world (WP2); technical standards and certification to quantify the impact of PETs (WP3); will produce online tools to allow researchers to audit deployed PETs (WP4); and undertake a broad programme of outreach and engagement to inform practice in policy, industry and academia (WP5). The Fellowship will thereby provide a framework to make research using digital traces safe and reliable; support data-driven policy interventions that rely on anonymised administrative data; and inform the regulation of underlying AI technologies-such as generative AI for synthetic data.

DRIVE-Health will train a minimum of 85 PhD health data scientists and engineers with the skills to deliver data-driven, personalised, sustainable healthcare for 2027 and beyond. Co-created with the NHS Trusts, healthcare providers, patients, healthtech, pharma, charities and health data stakeholders in the UK and internationally, it will build on the successes of its King's College London seed-funded and industry-leveraged pilot. Led by an established team, further growing the network of funding partners and collaborators built over the past four years, it will leverage an additional £1.45 of investment from King's and partners for every £1 invested by EPSRC. A CDT in data driven health is needed to deliver the EPSRC Priority for Transforming Health and Healthcare, EPSRC Health Technologies Strategy, and on challenges laid out in the UK Government's 2022 Plan for Digital Health and Social Care envisaging lifelong, joined-up health and care records, digitally-supported diagnoses and therapies, increasing access to NHS services through digital channels, and scaling up digital health self-help. This ambition is made possible by the increasing availability of real-world routine healthcare data (e.g. electronic health care record, prescriptions, scans) and non-healthcare sources (e.g. environmental, retail, insurance, consumer wearable devices) and the extraordinary advances in computational power and methods required to process it, which includes significant innovations in health informatics, data capture and curation, knowledge representation, machine learning and analytics. However, for these technological and data advances to deliver their full potential, we need to think imaginatively about how to re-engineer the processes, systems, and organisations that currently underpin the delivery of healthcare. We need to address challenges including transformation of the quality, speed and scale of multidisciplinary collaborations, and trusted systems that will facilitate adoption by people. This will require a new generation of scientists and engineers who combine technical knowledge with an understanding of how to design effective solutions and how to work with patients and professionals to deliver transformational change. DRIVE-Health's unique cohort-based doctoral research and training ecosystem, embedded across partner organisations, will equip students with specialist skills in five scientific themes co-produced with our partners and current students: (T1) Sustainable Healthcare Data Systems Engineering investigates methods and frameworks for developing scalable, integrated and secure data-driven software systems (T2) Multimodal Patient Data Streams will enable the vision of a highly heterogeneous data environment where device data from wearables, patient-generated content and structured/unstructured information from electronic health records can combine seamlessly (T3) Complex Simulations and Digital Twins focuses on the paradigm of building simulated environments, including healthcare settings or virtual patients, to make step-change advances in individual predictive models and to inform clinical and organisational decision-making. (T4) Trusted Next-Generation Clinical User Interfaces will place usability front and centre to ensure health data science applications are usable in clinical settings and are aligned with users' workflows (T5) Co-designing Impactful Healthcare Solutions, is a cross-cutting theme that ensures co-production and co-design in the context of health data science, engagement with stakeholders, evaluation techniques and achieving maximum impact. The theme training will be complemented with a cohort and programme-wide approach to personal, career, professional and leadership development. Students will be trained by an expert pool of 60+ supervisors from KCL and across partners, delivering outstanding supervision, student mentoring, opportunities, research quality and impact.