University of Surrey 2022 Uncertainty Quantification for Robust AI through Optimal Transport

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 Professor 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. 

• Table of Content
• About University of Surrey
• Uncertainty Quantification for Robust AI through Optimal Transport
• Aim and Benefits of Uncertainty Quantification for Robust AI through Optimal Transport
• Requirements for Uncertainty Quantification for Robust AI through Optimal Transport Qualification
• Application Deadline
• How to Apply

About University of Surrey

The University of Surrey was established on 9 September 1966 with the grant of its Royal Charter, but its roots go back to a late 19th-century concern to provide greater access to further and higher education for the poorer inhabitants of London. Since the University's founding in the 1960s, and before that at Battersea College, our community has thrived through strong connections and collaboration with the outside world. We've formed close partnerships with other institutions and businesses, reaching across geographic boundaries, and used those relationships to bring potential to life.... continue reading

Uncertainty Quantification for Robust AI through Optimal Transport

Aim and Benefits of Uncertainty Quantification for Robust AI through Optimal Transport

• 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) 

Requirements for Uncertainty Quantification for Robust AI through Optimal Transport Qualification

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).

This studentship is for UK, EU and overseas students.

IELTS requirements: If English is not your first language, you will be required to have an IELTS Academic of 6.5 or above (or equivalent), with no sub-test score below 6.  

Application Deadline

How to Apply

For more details, visit University of Surrey website.