Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

PI: Bergmann, Jeroen

Department: Engineering Science (DF)

In my computer science DPhil, I utilised open data from regulators to manage security, privacy, and safety risks in information systems. For this application, I will apply my DPhil research to the field of online support for medical device development. Determining the risk that medical devices pose to patients is important from a general safety point of view. Manufacturers of these devices need to assess the inherent risks in their devices and develop an effective management plan to address them. The company RegMetrics has developed an online platform that helps people navigate the medical device regulations. This project will explore the integration of my research on machine learning approaches to regulatory compliance into the existing RegMetrics platform to make it available to users. The project will assess the effectiveness and usability of these novel approaches through stakeholder engagement and feedback.

Harnessing data from water 'ATMs' to improve community health in rural Kenya                 Thomson, Patrick             SOGE                "In rural sub-Saharan Africa, as part of efforts to tackle dramatically inadequate water services, 'water ATMs' are being increasingly installed at public water points. These improve clean water supply by allowing real-time remote monitoring of water flow, and pre-payment. This is based on high-resolution flow metering, and tag-based water credit payments, with usage data relayed through the mobile data network.

EPSRC-funded doctoral research for this project revealed that: ATMs measure flow accurately and work robustly; communities benefit from ATMs as they measurably improve water access to users throughout the day; and service providers benefit from improved revenue collection as pre-payments are accountable, therefore improving sustainability. Because ATMs now accurately provide water flow and use data, the research also found how water collection varies over time of day, location, and season. This showed that collection of clean water from ATMs decreases in rainy seasons, which in turn leads to more disease. The ability to reduce water price over these periods would mitigate this by incentivising users to keep using clean water. From this finding, a cheap intervention has been designed, where it is hoped price reductions will both reduce disease risk to ATM users while maintaining revenue levels for the service provider, thus supporting the service provider's business model and sustainability.

This project will allow the research findings to make the jump to real-world impact in Kenya. It will support a service provider to get the most out of their water ATMs and adapt to the seasonal pressures they face. By testing this pricing intervention in Kenya, the project aims to improve overall service delivery to users, reduce the risk of disease, and build resilience to the impacts of climate change in some of the world's poorest communities. From this, it is hoped that the project will provide a blueprint for other service providers across sub-Saharan Africa.

Related themes