Crowd analysis and simulation using deep learning and big data analysis

Project description

This project is to look into the fundamental crowd motions in different environments including indoor/outdoor scenarios. The goal of this project is to propose a series of deep learning models and mathematical frameworks to capture the crowd motions for the purposes of analysis and simulation. The research falls into the category of data-driven crowd analysis where data is intensively used for analysis as compared to traditional empirical modelling where concise mathematical models are made trying to capture the complex structure in the data. However, because of the high complexity, more data (big data) is needed and meanwhile corresponding algorithms are to be developed. Deep learning has become a major force in many areas but not yet in this one, thus has a huge potential.

Entry requirements

Applications are invited from candidates with a minimum of a UK upper second class honours degree (2:1), and/ or a Master's degree in a relevant subject. We also recognise relevant industrial and academic experience.

How to apply

Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section of your application, the name of the PhD you wish to apply for is 'Crowd analysis and simulation using deep learning and big data analysis' as well as Dr He Wang as your proposed supervisor. In the funding section, please state 'School of Computing Funded Studentships' as your sponsor.

If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.

We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates.  All scholarships will be awarded on the basis of merit.

If you require any further information please contact the Graduate School Office
e:, t: +44 (0)113 343 8000.