Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics

Progress in sensing, computational power, storage and analytic tools has given us access to enormous amounts of complex data, which can inform us of better ways to manage our cities, run our companies or develop new medicines. However, the 'elephant in the room' is that when we act on that data we change the world, potentially invalidating the older data. Similarly, when monitoring living cities or companies, we are not able to run clean experiments on them - we get data which is affected by the way they are run today, which limits our ability to model these complex systems. We need ways to run ongoing experiments on such complex systems.

We also need to support human interactions with large and complex data sets. In this project we will look at the overlap between the challenge someone faces when coping with all the choices associated with booking a flight for a weekend away, and an expert running complex experiments in a laboratory.

The project will test the core ideas in a number of areas, including personalisation of hearing aids, analysis of cancer data, and adapting the computing resources for a major bank.

To catalyse a long term culture change and build skills capacity in Data Science, the School is planning to co-locate up to 30 J.P. Morgan staff and the Glasgow Hub of The DataLab in the same building as the academics with an Innovation Hub building in construction. Further data providers are Glasgow Polyomics, the Urban Big Data Centre and Skyscanner. The long term legacy in data science will be achieved through changing practice together with data owners, but also by sharing the tools developed in open-source resources that can be developed by others.

Closed-loop data science is a new, challenging topic which will take time for both researchers and end-users to grasp. This project will be closely integrated with two data-rich companies (J.P. Morgan, Skyscanner) who will ground the research in a real world industrial context, as well as academic institutions from life sciences and urban studies (Glasgow Polyomics, Urban Big Data Centre). We will exploit industrial collaborations via the QuantIC innovation hub and the DataLab Innovation Centre. In each case, this project leverages existing tight-knit links to local teams. The core research topics have applications with multiple end-users (both companies/data providers, as well as their customers), allowing us to test theories in multiple domains, and transfer experience between application areas. For instance, Skyscanner customers will benefit from more effective recommendations and search results that are less biased from closed-loop interactions.

We have embedded staff with end-users (in J.P. Morgan's case they are embedded with us), and there will be separate task groups and meetings on projects with specific data providers. Each post-doc will also act as a liaison point for at least one of the external data providers. 50% of a Grade 7 PDRA will be supported at the academic data providers (UBDC, Polyomics). We will arrange periodic away days for all staff students and end-users to discuss new developments, and look for cross-fertilisation of ideas among application areas and data providers.

A senior RA will support adaptation of research-quality systems to work within the constraints of the target systems of the data providers. To ensure a lasting impact, this RA will ensure the quality of the open-source software made available for immediate use with project partners, and to manage the dissemination to the broader technical community, ensuring its longer-term legacy. Project partners will provide ongoing feedback on compliance with current standards for deployability with their teams. The impact process will be supported by Jill Ramsay, the SoCS Business Development professional, as well as BD staff at the DataLab and QuantIC Hubs.

The UoG is building a £100M interdisciplinary Research & Innovation Hub - a commitment to excellence which will include a Data Science Centre to provide an environment in which researchers involved in the generation, interrogation, analysis and visualisation of large, complex datasets can come together to stimulate new cross-disciplinary collaborative projects. Creating a culture of co-creation is at the heart of the Innovation Hub vision; embedding the Data Science Centre in the hub will enable to fully support and sustain this project and its plans for co-creation well beyond the initial EPSRC funding.