Closed-Loop Data Science
Closed-Loop Data Science
We are advertising the following 4 vacancies:
1 Post-Doctoral Research Associate/Fellow position in Computing Science on Data Systems position E20386
To make a leading contribution to the EPSRC funded project “Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics” coordinated by PI Prof. Roderick Murray-Smith, working with Dr Nikos Ntarmos (line manager) and Dr Christos Anagnostopoulos.
Specifically, the job requires expert knowledge in large scale distributed systems, (big) data management and processing systems, and resource management and scheduling in distributed environments. The work will involve working closely with the technical team at JP Morgan’s Glasgow office, and there will be opportunities during the project for internships at JP Morgan.
The successful candidate will also be expected to contribute to the formulation and submission of research publications and research proposals as well as help manage and direct this complex and challenging project as opportunities allow.
- Reference Number E20386
- Location Gilmorehill Campus / Main Building
- College / Service COLLEGE OF SCIENCE & ENGINEERING
- Department SCHOOL OF COMPUTING SCIENCE
- Job Family Research And Teaching
- Position Type Full Time
- Salary Range £35,210 - £39,610 / £43,266 - £50,132
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.
2. Ph.D. scholarship (42 months, with stipend and fees for EU/UK citizens) on Reinforcement learning in closed-loop data science. supervised by Professor Roderick Murray-Smith and Dr Bjorn Jensen (Deadline 15th May 2019)
This studentship will explore reinforcement learning techniques in 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.
Reinforcement learning is a key tool in adapting closed-loop system performance. The student will have the opportunity to apply techniques together with applications problems from our research collaborators, which include Amazon, Moodagent, Skyscanner, JP Morgan and Widex.
Conversational AI interfaces are beginning to allow people to communicate naturally with computers for the first time. This PhD, in partnership with Moodagent, focuses on intelligent music agents. At a basic level, this involves research and development of task-based music agents to allow people to interact with music (playing music, creating playlists, searching for music, etc…). Further, the goal is the development of an ‘intelligent’ agent that can incorporate contextual recommendations (music for running, music for a beach party, etc…) in a conversational context. For example, the context may include multiple users interacting (to create a playlist for a party) and the agent may help mediate the interactions. Going deeper, the aim is to develop conversational music intelligence that allows an agent to not simply perform tasks, but to converse with an agent about music. Music is a personal and subjective topic that is often vaguely defined, which make conversation in this domain both natural as well as an important research challenge. The explanatory descriptions from conversations about music will be used to explore research on deeper language-based understanding of music in order to improve quality and explainability of music retrieval and recommendation systems. This PhD will explore conversational music interactions in a cross-cultural and multilingual environment with Moodagent users in Denmark and India and will study key differences for this new and emerging group of users.