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Cloud-based Computational Intelligence Approaches to Machine Learning and Big Data Analytics


Project Description

Cloud computing makes it possible to build scalable machine learning systems for processing massive amounts of complex data, be them structured or unstructured, real time or historical, the so-called Big Data. Publicly available cloud computing platforms have been made available, for instance, Amazon EC2, EMR, and Google Compute Engine. More importantly, open source APIs and libraries have also been developed for ease of programming on the cloud, for instance, Cascading, Storm, Scalding, and Apache Spark.

Meanwhile, computational intelligence approaches, examples of which include evolutionary computation, immune-inspired approaches, and swarm intelligence, are also employed to develop scalable machine learning and data analytics tools.

In this project we will explore the development of synergistic machine learning systems that make use of both the computational power of cloud and the intelligent processing ability of computational intelligence.

We aim to answer the following questions:

1. How to adapt existing computational intelligence algorithms, especially population-based algorithms, into established and emerging cloud computing paradigms.
2. How to combine computational intelligence and cloud computing in a synergistic manner to achieve better learning and data mining performance.
3. Can we develop novel cloud-specific population-based machine learning algorithms that outperform adapted algorithms for both real time and batch data processing?
4. How to apply the developed cloud machine learning algorithms to emerging real-world problems, for instance, online recommendation, social media analysis, and fraud/anomaly detection from online user behaviours.

The successful candidate should have, or expect to have, an Honours Degree at 2.1 or above (or equivalent) in Computing Science or related disciplines.

Knowledge of: Essential: Basic understanding of cloud computing concepts; Knowledge of machine learning; programming experience in Java, Scala, Python, or Ruby.

Desirable: familiar with MapReduce. Knowledge of Apache Spark. Knowledge of evolutionary computing and immune-inspired algorithms.

Funding Notes

There is no funding attached to this project, it is for self-funded students only

References

APPLICATION PROCEDURE:
This project is advertised in relation to the research areas of the discipline of Computing Science. Formal applications can be completed online: http://www.abdn.ac.uk/postgraduate/apply. You should apply for PhD in Computing Science, to ensure that your application is passed to the correct College for processing. NOTE CLEARLY THE NAME OF THE SUPERVISOR and EXACT PROJECT TITLE ON THE APPLICATION FORM. Applicants are limited to applying for a maximum of 2 projects. Any further applications received will be automatically withdrawn.

Informal inquiries can be made to Prof G Coghill ([email protected]) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Graduate School Admissions Unit ([email protected]).

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