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Despite significant progress in HIV management and prevention, 1.8 million new HIV infections were recorded worldwide in 2016, with many settings observing increasing numbers of new HIV infections, predominantly in key affected populations. Despite the development of revolutionary evidence-based HIV prevention interventions, combating the HIV/AIDS epidemic still remains a major global health challenge. Key populations groups, namely, men who have sex with men (MSM), transgender people, people who inject drugs, prisoners and sex workers, are at greatest risk of being affected by HIV and they frequently lack adequate access to appropriate services. Stigma and discrimination are among the primary barriers to HIV prevention, treatment and support; key populations in particular face significant stigma in many settings. Indonesia has the third largest number of people living with HIV (PLWH) in Asia and the Pacific; with 620,000 PLWH and 48,000 new infections recorded in 2016. Key populations are most affected and at greatest risk of HIV. In 2016, HIV prevalence estimates were 25.8% in MSM (compared to 8.5% in 2011), new infections are increasing significantly and rapidly in MSM in Indonesia but more reportedly stable in other key populations. In major cities Denpasar in Bali and Jakarta, nearly one in three MSM are infected with HIV. Indonesia is observing worsening stigma and discrimination against MSM, transgender people and PLWH which is significantly slowing the country's HIV response. This rise in anti- LGBT rhetoric adds great challenges of allowing a human rights-based approach to sexual health care and HIV prevention to hard-to-reach key populations and counteracting HIV prevention and treatment targets. Innovative and sensitive methods of engaging at risk communities to improve access and uptake to services, without increasing their vulnerability to abuse, are greatly needed. Tailored mobile phone, internet-based programs may be a critical way to engage key populations like MSM who are understood to be a technologically/mobile smartphone literate population. We propose a tailored, innovative HIV prevention model to strengthen HIV prevention services and accelerate the response to a growing burden of HIV in MSM, in Indonesia. A model appropriately adapted through at-risk population engagement, from one that has demonstrated success in dramatically reducing new HIV infections in MSM in London. We propose assessing the feasibility, acceptability and impact of; (1) a mobile-phone text-message based digital-health HIV risk-reduction intervention, tailored to the MSM population at high risk of HIV in Indonesia; developed through at-risk population engagement and needs assessment. (2) integrating point of care/rapid HIV and HIV monitoring diagnostics in an existing sexual-healthcare service, to also determine the proportion of very early HIV acquisition in new HIV diagnoses to further guide HIV treatment and prevention. (3) integrating evidence-based prevention interventions - immediate antiretroviral treatment as prevention to all individuals recently diagnosed/living with HIV and oral Pre-Exposure Prophylaxis provision (a proven HIV preventative medicine) to MSM at risk of HIV in an existing sexual-healthcare service. Can the prevention model have an impact on; increasing HIV testing coverage in MSM at risk of HIV; reduce undiagnosed and late diagnoses of HIV infection in this at-risk population; have an impact on reducing new HIV infections and improve retention in HIV care and antiretroviral treatment (ART) adherence in people diagnosed and living with HIV. Ultimately to contribute to the UNAIDS/WHO goals in ending AIDS as a global public health threat in this region, to aid disease prevention and improve health in this population and consequently have economic benefits for the country as a whole.

How would your life change if you could not use your hands effectively? An estimated 2.7 million people in the UK suffer from a debilitating loss of manual dexterity, which has a dramatic effect on their quality of life. The wrist is the most common site of traumatic injury in the human body, but despite this, treating hand and wrist injuries remains a challenge for the hand surgeon and therapist. Methods of treatment often vary from clinician to clinician and the success of their outcomes is difficult to assess. A reason for this is the complexity of the wrist - it is comprised of eight bones that move relative to those in the forearm and the hand. One method for determining the effects of treatment is to study the forces in the muscles and the motions that result. This can be done using computer models. However, many parameters are needed in order to create a computer model that accurately represents the complex anatomy. An alternative way in which we can evaluate the interactions between the many joints in the hand and wrist, is through the use of joint motion simulators. These simulators replicate joint movement in human specimens by applying forces to the tendons, enabling us to measure the forces in the muscles and joints. This allows us to compare the effects of different surgical and therapeutic procedures in a way that is simply not possible with patient volunteers. In this way we can objectively assess treatment regimens, testing the biomechanical outcomes, before applying them to patients. My research group has developed and tested a joint motion simulator for the wrist that includes six major muscles. However, it is well known that the function of the muscles that control the fingers is highly linked to those that control the wrist. Therefore, the aim of this project is to use our expertise to create a new custom-made joint motion simulator for the wrist that also includes the finger muscles. This work will create a unique and innovative device that possesses greater realism and functionality by enabling us to replicate the motions of the fingers in addition to the wrist. Motors will be used to create the effects of the muscles. Specialised cameras will be used to monitor and control the motion of the joints in real-time. Testing will be performed in collaboration with colleagues in surgery, who will also ensure the clinical impact of the work. The result of this research will produce a device that can be used in the design and testing of implants and other orthopaedic devices, the validation of computational models of the musculoskeletal system, the design of prosthetics, and the training of clinicians.

Machine learning (ML) systems are increasingly being deployed across society, in ways that affect many lives. We must ensure that there are good reasons for us to trust their use. That is, as Baroness Onora O'Neill has said, we should aim for reliable measures of trustworthiness. Three key measures are: Fairness - measuring and mitigating undesirable bias against individuals or subgroups; Transparency/interpretability/explainability - improving our understanding of how ML systems work in real-world applications; and Robustness - aiming for reliably good performance even when a system encounters different settings from those in which it was trained. This fellowship will advance work on key technical underpinnings of fairness, transparency and robustness of ML systems, and develop timely key applications which work at scale in real world health and criminal justice settings, focusing on interpretability and robustness of medical imaging diagnosis systems, and criminal recidivism prediction. The project will connect with industry, social scientists, ethicists, lawyers, policy makers, stakeholders and the broader public, aiming for two-way engagement - to listen carefully to needs and concerns in order to build the right tools, and in turn to inform policy, users and the public in order to maximise beneficial impacts for society. This work is of key national importance for the core UK strategy of being a world leader in safe and ethical AI. As the Prime Minister said in his first speech to the UN, "Can these algorithms be trusted with our lives and our hopes?" If we get this right, we will help ensure fair, transparent benefits across society while protecting citizens from harm, and avoid the potential for a public backlash against AI developments. Without trustworthiness, people will have reason to be afraid of new ML technologies, presenting a barrier to responsible innovation. Trustworthiness removes frictions preventing people from embracing new systems, with great potential to spur economic growth and prosperity in the UK, while delivering equitable benefits for society. Trustworthy ML is a key component of Responsible AI - just announced as one of four key themes of the new Global Partnership on AI. Further, this work is needed urgently - ML systems are already being deployed in ways which impact many lives. In particular, healthcare and criminal justice are crucial areas with timely potential to benefit from new technology to improve outcomes, consistency and efficiency, yet there are important ethical concerns which this work will address. The current Covid-19 pandemic, and the Black Lives Matter movement, indicate the urgency of these pressing issues.