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Mining data stream in high dimensional space and regression domain (Advert Reference: SF18/CIS/SEKER)

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

It is estimated that 2.5 quintillion bytes of data from a wide range of sources are generated daily, which is called “big data” . Examples of the main contributors to this avalanche of “big data” is wired and wireless sensor technologies, connected devices, internet of things and publically available databases of rich information, which are all expected to revolutionize almost every aspect of science and the humanities.

Pattern recognition, classification, clustering, regression and feature selection are the most widely used approaches to analyse big data from which it is expected to derive life-saving and profitable knowledge. In line with the exponential increase in the size, complexity, dynamism and uncertainty of this data, a pressing need has emerged for the development of faster, more accurate and reliable methods to address “big data” challenges in the digital age.

Among all these challenges, quantitative prediction has become more important and attracting due to the sensor technologies as it is estimated that 300 billion sensors are making lifestyle enhancements in our daily lives in 2020. However, regression methods that enable quantitative prediction over such an ultra-high dimensional space and its feature selection in supervised or unsupervised manners don’t seem to have been studied enough, in particular, in data stream. Therefore, PhD candidate is expected to develop non-linear and robust regression methods, supervised and unsupervised feature selection techniques in big data domain, in particular, in streaming data environment, while considering online instance selection methods. The project also involves an industrial partner and therefore addresses real-world problem at large scale.

We are looking for a candidate who has a strong analytical skills, background in machine learning, optimisation and pattern recognition methods, and experience of programming in R, Python or similar. Previous knowledge in big data platforms and mining streaming data sets is desirable.

Please note eligibility requirement:

* Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
* Appropriate IELTS score, if required

For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF18/CIS/SEKER) will not be considered.

Start Date: 1 March 2019 or 1 June 2019 or 1 October 2019

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University hold an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.


Funding Notes

This is an unfunded project

References

V. Uslan and H. Seker, “Quantitative prediction of peptide binding affinity by using hybrid fuzzy support vector regression”, Applied Soft Computing, Vol.43, pp: 210-221, June 2016

F. Sarac and H. Seker, “An Instance Selection Framework for Mining Data Streams to Predict Antibody-Feature Function Relationships on RV144 HIV Vaccine Recipients”, IEEE SMC 2016, Budapest, Hungary, 9-12 October 2016.

A. Dreder, M.A. Tahir, H. Seker and M.N. Anwar, “Discovering differences in gender-related skeletal muscle aging through the majority voting-based identification of differently expressed genes”, International Journal of Bioinformatics and Biosciences, Vol.6(2), pp: 1-14, June 2016

A.B. Orun, H. Seker, J.L. Rose, A. Moemeni and M. Fidan, “A quantisation of cognitive learning process by computer graphics-games: towards more efficient learning models”, Open Access Library Journal, 3:e2329, January 2016. http://dx.doi.org/10.4236/oalib.1102329

F. Sarac, V. Uslan, H. Seker and A. Bouridane, “A Supervised Feature Selection Framework in relation to Prediction of Antibody Feature-Function Activity Relationships in RV144 Vaccines”, IEEE SMC 2016, Budapest, Hungary, 9-12 October 2016.

V. Uslan and H. Seker, “Binding Affinity Prediction of S. Cerevisiae 14-3-3 and GYF Peptide-Recognition Domains Using Support Vector Regression”, IEEE EMBC 2016, Florida, USA, 16-20 August 2016.

F. Sarac, V. Uslan, H. Seker and A. Bouridane, “Unsupervised Selection of RV144 HIV Vaccine-Induced Antibody Features Correlated to Natural Killer Cell-Mediated Cytotoxic Reactions”, IEEE EMBC 2016, Florida, USA, 16-20 August 2016.

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