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I am keen to supervise self-motivated PhD students who have similar research interests to mine; i.e., Machine Learning, Pattern Recognition, Machine Perception and their applications to Intelligent System Development. You can find my current PhD Research Project Suggestions, where the background knowledge and skills required for PhD studies are also specified as pre-requisite. You are strongly encourage to develop your own research proposal based on either my PhD project suggestions or your self-proposed project if accepted by me.

If you are interested in working with me for your PhD studies at The University of Manchester, you are encouraged to contact me informally for the first instance. Prior to contacting me, however, all applicants (especially non-EU applicants) should first check the funding information below to see if you meet the funding requirements and have been following a proper inquiry procedure described below.

When you contact me, you'll need to send me your CV and other background materials of yours (e.g., your transcripts at different levels, your UG final year project report, your MSc thesis and selected publications if you have). Once we have an agreed research topic based on a proposal you draft, you can kick off your formal application via our Online Application website. For the formal application, you can find the detailed information from The School PhD Student and The University Research Student pages. During the application process, please direct all non-acadmic quaries, e.g., English language qualification for overseas students, to our CS School Admission Office.  

Funding Information for Prospective Applicants

The following funding information is only for your convenience. If you have any questions regarding funding and/or want the latest funding information, please directly consult with our CS School Admission Office.

  • UK/EU Applicants

    At the University of Manchester, there are various financial support opportunities for a prospective UK/EU PhD student. In School of Computer Science, qualified UK and EU applicants (who at least have been awarded the first-class honour in UG or the distinction in MSc, ideally both) may be awarded a full studentship including tuituion fee and maintenance for our normal three-year PhD programme. The school funding is allocated on a competitive basis; qualified candidates are shortlisted and interviewed to be awarded such a scholarship. In addition, the University offers alternative scholarship, e.g, President's Doctoral Scholar Award and FSE Faculty Studentship. You can access to the The University Postgraduate Funding page to find the latest funding information as well as checking your eligibility. If you have any questions to funding, please don't hesitate to direct your inquires to our CS School Admission Office.

  • International (non-EU) Applicants

    To apply for any funding described below, a candidate must gain the English qualification before their application.

    For an international, i.e., non-EU, student, who at least holds a 1st class honour or distinction degree in MSc or equivalent (ideally both), our school could offer you a scholarship that waives the full tuition fee each year over three years. The school funding is allocated on a competitive basis; qualified candidates are shortlisted and interviewed to be awarded such a highly competitive scholarship. Note that  our school can offer NOTHING beyond this scholarship, which means you have to secure the maintainance by yourself.

    The following information is especially for an outstanding non-EU applicant who needs a FULL studentship including tuition fee and maintainance. Our university and EPS faculty offer extremely competitive President's Doctoral Scholar Award and FSE Faculty Studentships for elite students including qualified non-EU applicants. For your information, "ELITE" means that one has been rated to top 3% in their UG/PGT classes in terms of academic performance,  published papers in prestigious journals of a high impact factor/top conferences (In practice, the abvoe two requirements are essential for success), won non-trivial academic award(s) and so on. The selection process undergoes at two levels: school and faculty/university; that is, each school can only nominate up to three candidates no matter how many applications are received. Then a panel at faculty/university level makes the final decision after assessing all submissions from different schools. The deadline in CS is normally 1st December/Febuary for the first/second round and enrollment time must be next September. Occasionally, there are also some chances of full scholarships offered by our school. If a non-EU student needs a FULL studentship, you should first check with The School Postgraduate Funding, FSE PhD Funding and The University Postgraduate Funding pages.

    For Chinese applicants who graduate from one of top-36 universities in China, they can also apply for the joint scholoarship between University of Mancheste (UoM) and Chinese Scholarship Council (CSC). The UoM-CSC Scholarship provides a full studentship and the essential requirements include an MSc degree in distinction equivalent and at least one paper published in top conferences and prestigious journals of a high impact factor (more details can be found from the FSE PhD Funding website). The selection process also undergoes at two levels: school and faculty/university.  The deadline in CS is normally 1st of January and the enrollment time must be next September.

For non-EU applicants, unless you can secure all other funding by yourself (provided you meet the requirement of waiving tuition fee) or you meet all the criteria of a full scholarship offered by our university and EPS faculty, please refrain yourself from making any inquiry (otherwise, it is likely to waste your efforts and time)! In other words, any non-EU applicant should explicitly confirm they meet the funding requirement when they contact me in the first time.

PhD Students Supervised in Manchester

As a main supervisor, I am currently supervising the following PhD students:

  • Danny Wood (2015-present) 
    Theoretic Aspects of Recurrent Neural Networks

  • Mike Phuycharoen (2015-present) 
    Biological Information Extraction via Representation Learning

  • William Woof (2016-present) 
    Deep Learning on Semi-structural Data and Its Applications to Video Game AI

  • Hao Zhou (2016-present) 
    Domain Shift Adaptation for Speech Emotion Recognition 

  • Jonathan Crawfold (2017-present) 
    Knowledge Transfer for Multi-task Reinforcement Learning  

  • Mingxuan Yi (2018-present) 
    Bayesian Deep Reinforcement Learning  

  • Ruoyu Sun (2019-present) 
    Multi-task Reinforcement Learning Applied in Autonomous Vehicles

In Manchester, the following students under my supervision (in the role of a sole/main supervisor) have been awarded a PhD degree:

  • Peizhi Shi (2013-2018)
    Learning Constructive Primitives for Procedural Content Generation  

  • Harits As Rosyid (2013-2017)
     Adaptive Serious Educational Game with Machine Learning 

  • Qian Wang (2013-2017)
    Zero-Shot Visual Recognition via Latent Embedding Learning

  • Fabio Zennaro (2013-2017)
    Feature Distribution Learning for Covariant Shift Adaptation Using Sparse Filtering

  • Ubai Sandouk (2012-2016)
    Concept Representation Learning for Multimedia Information Retrieval 

  • Jonathan Parkinson (2012-2016)
    Representation Learning with a Temporally Coherent Mixed Representation

  • Tamas Madl (2011-2015)
    Bayesian Mechanisms in Spatial Cognition: Towards Real-World Capable Computational Cognitive Model of Spatial Memory

  • David Buckley (2011-2015)
    Skill Capture in First-Person Shooters

  • Jonathan Roberts (2009-2013)
    Learning-based Procedural Content Generation

  • Ahmad Salman (2007-2011)
    Learning Speaker-Specific Characteristics with Deep Neural Architectures

  • Yun Yang (2006-2010)
    Clustering Ensemble and Applications to Temporal Data Clustering

  • Arslan Shaukat (2005-2009)
    Emotional State Anaysis and Recognition from Speech Signals

  • Shihai Wang (2005-2009)
    Boosting Learning Applied to Facial Expression Recognition

This page was last updated at 03:35pm, July 4th, 2019.