麻豆果冻传媒

As a mathematics and statistics PhD student at 麻豆果冻传媒, you will benefit from:

• a supervisory team comprising two or sometimes three members of academic staff. Depending on your research specialism you may also have an additional supervisor from another school, another research institution, or an external partner from government or industry. 
• desk space and access to a desktop PC, either in one of the postgraduate offices on the 6th floor of the award-winning Cockcroft Building, or within the adjacent Huxley Building.  
• access to a range of electronic resources via the university’s Online Library, as well as to the physical book and journal collections housed within the Aldrich Library and other campus libraries. 
• use  of various state-of-the-art research facilities on the Moulsecoomb site.

You will be part of a small group of mathematics and statistics PhD students, thereby having excellent access to supervisory teams, whilst being situated amongst a larger cohort of PhD students within the university, with school-based students across computing and engineering facilitating further potential for interdisciplinary interactions. You will be aligned with the Computing and Mathematical Science Research Excellence Group. 

The 麻豆果冻传媒 Doctoral College offer a training programme for postgraduate researchers, covering research methods and transferable (including employability) skills. Attendance at appropriate modules within this programme is encouraged. Participation in ongoing subject-specific seminar programmes at the University of 麻豆果冻传媒 is also highly recommended.

We are particularly interested in projects that align to one of our specialist themes:

• The Computational methods theme focuses on the development of numerical methods for solving problems in applied mathematics and engineering. Avenues of interest include finite element methods, boundary element methods and the machinery which underpins these, such as numerical methods of linear algebra. These methods have been applied to problems as diverse as modelling the sound field radiated from a loudspeaker to modelling the formation and growth of cavities in the spinal cord.
• The Discrete Mathematics theme is interested in the development and application of discrete mathematical techniques, particularly as applied within computer science. Areas of interest include logic, formal languages, algebraic structures, algorithms, graph theory, combinatorial and computational geometry and topology. These methods have been applied to the development of visual logics to assist human reasoning, the development of information visualisation algorithms which automatically generate diagrams, and efficient knot detection with biological applications.
• The statistics and operational research theme has a wide range of research interests from theoretical probability to applied statistics and rigorous machine learning. Examples of current work are the application of financial and operational research methods for pricing financial and energy instruments and analysing investment incentives, the development of statistical methodologies for analysing big data studies, the development of marginal regression models with correlated discrete response variables, the theoretical analysis of online prediction models and the application of topological data analysis methods.

I am interested in supervising postgraduate research projects related to mathematically intensive methods of machine learning. My main area is sequential online forecasting, particularly prediction with expert advice. I am also interested in reinforcement learning (especially as a generalisation of online forecasting), kernel methods and topological data analysis.

I supervise PhD students who use numerical methods (such the boundary element method and finite element method) to solve problems in applied mathematics.

PhD projects that I am interested in supervising include using combined boundary element and finite element methods for modelling the motion of biological cells due to chemotaxis, the use of the finite element method to model deformations of the spinal cord (in relation to the medical condition syringomyelia) and the use of the finite element method in image analysis (such denoising images and segmenting images).

My area of expertise is Bibliometrics and Scientometrics. I would be interested in supervising PhD/Master/Undergraduate students at any angle of Bibliometrics and Scientometrics and linking them with data analytics and data science. Some examples of specific potential topics (but are not limited to):

• Analyse and examine the relation between economic factors and the amount of scientific productivity within countries, academic institutions, geographic regions and subject areas.
• Examine the citation flows between countries or organisations that collaborate.
• Analysis of Scientific Research Collaborations (institutional, national, international)
• Research on Sustainable Development Goals (SDGs)
• Bibliometric horizon scanning methodology for identifying emerging topics.
• Analyse the relationship between international collaborations and the mobility of researchers.
• Conduct gender analyses based on publication data, especially in the context of the SDGs.
• Creation and implementation of new scientometric indicators in open research infrastructures.
• Examine the open-access citation advantage.
• Analysis of predatory publishing as a threat to research integrity.
• Understand the research profiles of Higher Education Institutions and their connection with scientific productivity.
• Bibliometric mappings of countries, institutions, disciplines, etc.

These topics are general and can be more detailed once the students show an interest in any of them. I will consider any new proposals and discuss any ideas that come up.

In my papers, I analyse the citation increment among collaborating countries. I have also examined how citation patterns of disciplines and bibliographic database coverage may influence the citation flows between subject areas.

The impact of economic factors on countries' research productivity has been the subject of my recent publications. I used data from citation databases, UNESCO, and the OECD for this purpose. I confirmed the relationship between investment in research and scientific results using multiple linear regression.

As well as understanding the scientific contribution to Sustainable Development Goals (SDGs), I focus my research on the mechanisms to interpret this data, since different databases can produce different results.

I am interested in supervising postgraduate research students in categorical data analysis, multivariate analysis and high-dimensional statistics.聽

For further supervisory staff including cross-disciplinary options, please visit