Dr J Williamson
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Data science and machine learning are rapidly developing powerful tools to extract insights from large-scale, complex datasets. There is much focus on the algorithms that power inference, but significantly less focus on how humans are involved in the data science process. Data science often involves a pattern of query; fit model; predict; plot results; repeat. This is clumsy and makes it difficult for end users to explore, comprehend and make judgements. A more modern approach would have humans brought into a tightly coupled closed-loop with the inference processes, interactively exploring the beliefs compatible with data and directing the inference process to mine the seams of knowledge within. This PhD will be focused on applying these ideas to probabilistic models, where representation and communication of uncertainty in the results of an inference problem are particular importance.
This studentship will focus on producing interactive, animated representations of probabilistic models. The project will focus on:
• Establishing key interaction and animation “primitives” that can be linked to (potentially high-dimensional), probabilistic Bayesian models to represent their underlying uncertainty more effectively than static displays. These will form the analogues of techniques like error bars but which exploit active perception via closed-loop control of displays.
• Developing techniques to augment sample-based (e.g. Markov Chain Monte Carlo) inference algorithms to dynamically sample and cache results from user inputs to close the loop between explorative data visualisation and inference. The aim is to provide accelerated inference in regions of importance to (implicit) queries in close to real-time.
The PhD will develop interaction techniques which facilitate active perception of uncertain data for a variety of data types (e.g. temporal, spatio-temporal, high-dimensional vector space models) and efficient strategies to accelerate inference to provide relevant inference to explorations happening in real-time.
The successful candidate will have a strong interest/background in visualisation, human-computer interaction and/or Bayesian probabilistic modelling.
This Ph.D will take place within the EPSRC project “Closed-loop Data Science”, https://www.gla.ac.uk/schools/computing/research/researchsections/ida-section/closedloop/
Funding Notes
Funding is available to cover tuition fees for UK/EU applicants for 3.5 years, as well as paying a stipend at the Research Council rate (£14,777 for Session 2018-19).