Machine learning has been making decisions that affect our lives. Yet, we often cannot even tell whether they are uncertain about their decisions. In this project, we will develop Bayesian techniques with tools from the optimal transport theory to better represent and quantify uncertainties in machine learning models. While theoretical results are promising, the deployment of the optimal transport theory in a wide range of machine learning applications is limited due to its heavy computational burden. We will derive algorithms for uncertainty propagation and quantification based on computationally efficient approximate optimal transport methods. The resulted toolkit will be validated on a real-world clinical application and is transferable across a wide range of safety-critical AI applications.
The successful applicant will be supervised by Dr Yunpeng Li and co-supervised by Prof Wenwu Wang. The PhD student will be based at the Nature Inspired Computing and Engineering (NICE) research group in the Department of Computer Science at the University of Surrey. The student will also benefit from resources from the Centre for Vision, Speech and Signal Processing in the Department of Electrical and Electronic Engineering at the University of Surrey.
This is a 3-year project starting in January 2022.
- Full tuition fee covered (UK, EU and international)
- Stipend at £15,609 p.a. (2021/22)
- RTSG of £1,000 p.a.
- Personal Computer (provided by the department)
This opportunity is funded by the University of Surrey’s Doctoral College.
A Bachelor’s degree or above in Computer Science, Electrical Engineering, Statistics, Mathematics, Physics or similar (a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university).
English language requirements: IELTS Academic 6.5 or above (or equivalent) with 6.0 in each individual category.
Applications should be submitted via the Computer Science programme page on the “Apply” tab.
Please state clearly the studentship project at you would like to apply for.
Please prepare to submit your CV; degree certificates and transcripts; names of 2 referees (ideally uploading 2 references at time of application also); and research proposal (including examples of previous project work).