Closed-Loop Data Science

Closed-Loop Data Science

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, support for travel planning, analysis of cancer data, and adapting the computing resources for a major bank.
 

Partners

Partners

This project involves collaborative research with academic partners:  Glasgow Polyomics, the Urban Big Data Centre,  the University of Warwick, and industrial partners:  JP Morgan Technology & Operations, Glasgow, Skyscanner and Widex A/S, and is further supported by DataLab Scotland.
 

Vacancies

Vacancies

We will have several Ph.D. opportunities in this area. If you are interested, contact Roderick.Murray-Smith@glasgow.ac.uk Current concrete Ph.D. opportunities include:
 
 
1.  Ph.D. scholarship (42 months, with stipend and fees for EU/UK citizens) on modelling and evaluating closed loop interactions in recommender systems supervised by Dr Craig Macdonald (Deadline 7th December 2018)

Machine learning techniques are widely used to address many recommendation scenarios – such as suggesting a movie to watch on (e.g.) Netflix, or recommending a point-of-interest to visit in a city, often by learning from historical user data. However, recommendation systems can be influenced by what users have already been recommended and thereafter viewed/visited, rather than what these systems might have found to be relevant of their own accord –for instance, Netflix might start to recommend movies that are already popular from its previous recommendations.

Such an effect can be described as a filter-bubble or a closed-loop feedback, and has been typically avoided through introducing novel or serendipitous recommendations into the suggestions. However, the alternative use of approaches originating from closed-loop theory, such as intermittent control, have not been systematically investigated within recommender systems.

This PhD will be focussed on applying ideas and techniques from closed-loop theory to state-of-the-art recommender systems. The candidate will investigate the modelling and deployment of closed-loop recommender systems using new neural networks architectures in comparison and along traditional matrix factorization and BPR-based recommenders. The evaluation of the resulting systems will be conducted using both public benchmarks in recommender systems as well as within the experimental pipeline of some of our data partners in the EPSRC Closed-Loop Data Science project.

The successful candidate will have a strong interest/background in recommender systems, machine learning, and/or information retrieval.

 
We are advertising the following vacancies:
 
1 PDRA position in Computing Science on Data Systems position 020356  http://www.jobs.ac.uk/job/BIJ678/research-associate-fellow/
 
If you have interest in applying, please contact Roderick.Murray-Smith@glasgow.ac.uk
 

Academic Staff

Funded Projects

Funded Projects

EPSRC funded project: £3M, 2018-2022:  Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics