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Autonomous and Automated learning with Explainable Artificial Intelligence


   School of Engineering

  , Dr Pascal Meissner  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Artificial Intelligence has made great strides and is increasingly used throughout industry and our society however there are significant limitations in terms of what it can achieve. For example, the majority of methods are black box in nature meaning that they cannot explain themselves, and this limits the usefulness of these approaches and also raises ethical concerns. Research at the University of Aberdeen on explainable AI techniques have resulted in some breakthroughs in this area in a variety of areas (such as textual data analysis, robotics, and traditional data mining), and this project will look to build on this cutting edge research work and to form part of the PhD team here. 

The focus of the PhD will be in the area of Autonomous learning and Automated AI. Popular machine learning and deep learning techniques, especially supervised algorithms, are not suitable for autonomous learning due to their overdependence on large amount of labelled data that are not always available or are expensive to acquire. 

In addition, popular methods in Artificial Intelligence and reinforcement learning techniques that are used in robotics applications are not explainable and are not transparent in their learning, resulting in the AI being unable to adapt to new situations that it should encounter. This research will therefore focus in developing autonomy for the AI methods, with some significant progress in this area already.

In addition, this project will focus on the task of automating data analysis tasks, particularly for numerical data, but the techniques developed could be applied to other domains such as textual data analysis. The data mining process currently requires human interaction and guidance throughout the process, and this project will look to exploit artificial intelligence techniques in order to allow the automation of the data analysis and feature extraction tasks.

This project will look at the development of autonomous and automated learning techniques inspired by biological learning processes, and will build on a track record of success in this area and will work with a team of other researchers in the area of autonomous and automated AI (refer to cited references for more information).

This work has great commercial value and will likely be of interest to companies in the data analysis field.

Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Engineering, Physics, Computing Science.

Experience in: Computer coding, algorithms, mathematics, data analysis

APPLICATION PROCEDURE:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php

• Apply for Degree of Doctor of Philosophy in Engineering

• State name of the lead supervisor as the Name of Proposed Supervisor

• State ‘Self-funded’ as Intended Source of Funding

• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)

• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding

Informal inquiries can be made to Dr A Starkey () with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()


Funding Notes

This PhD project has no funding attached and is therefore available to students (UK/International) who are able to seek their own funding or sponsorship. Supervisors will not be able to respond to requests to source funding. Details of the cost of study can be found by visiting View Website

References


• Abdul Aziz, A & Starkey, A 2020, 'Predicting Supervise Machine Learning Performances for Sentiment Analysis Using Contextual-Based Approaches', IEEE Access, vol. 8, pp. 17722-17733.[ONLINE] DOI: HTTPS://DOI.ORG/10.1109/ACCESS.2019.2958702
• Ahmad, AU & Starkey, A 2018, 'Application of feature selection methods for automated clustering analysis: a review on synthetic datasets', Neural Computing and Applications, vol. 29, no. 7, pp. 317-328.[ONLINE] DOI: HTTPS://DOI.ORG/10.1007/S00521-017-3005-9
• Starkey, A & Ahmad, AU 2018, Semi-automated data classification with feature weighted self organizing map. in ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Institute of Electrical and Electronics Engineers Inc., pp. 136-141, 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017, Guilin, Guangxi, China, 29/07/17.[ONLINE] DOI: HTTPS://DOI.ORG/10.1109/FSKD.2017.8392964
• Ezenkwu, CP & Starkey, A 2019, Machine Autonomy: Definition, Approaches, Challenges and Research Gaps. in K Arai, R Bhatia & S Kapoor (eds), Intelligent Computing: CompCom 2019, Proceedings. Advances in Intelligent Systems and Computing, Springer , Cham, pp. 335-358, Computing Conference 2019, London, United Kingdom, 16/07/19.[ONLINE] DOI: HTTPS://DOI.ORG/10.1007/978-3-030-22871-2_24
• Ezenkwu, CP & Starkey, A 2019, 'Unsupervised Temporospatial Neural Architecture for Sensorimotor Map Learning', IEEE Transactions on Cognitive and Developmental Systems.[ONLINE] DOI: HTTPS://DOI.ORG/10.1109/TCDS.2019.2934643

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