About
Dr Muller is a lecturer in Computer Science at Swansea University.
Dr Bertie Muller
Senior Lecturer,
Computer Science
Office - 336
Third Floor
Computational Foundry
Bay Campus
Third Floor
Computational Foundry
Bay Campus
Areas Of Expertise
- Artificial Intelligence
- Technology Ethics
- Formal Languages
- Mathematical Logic
- Models of Concurrency
- Formal Verification
Publications
Research Highlights
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Williams, S., Ware, J., Müller, B., & Muller, B. (2019). Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia. In Artificial Intelligence in Health-47). Springer Nature Switzerland AG.
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Williams, S., Ware, J., & Muller, B. (2018). Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity. In Proceedings of the First Joint Workshop on AI in Health organized as part of the Federated AI Meeting (FAIM 2018), co-located with AAMAS 2018, ICML 2018, IJCAI 2018 and ICCBR 2018 (pp. 212-223). AIH 2018 - Joint Workshop on AI in Health.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa43607
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Williams, S., Müller, B., & Muller, B. (2017). Agents and Dementia — Smart Risk Assessment. In Multi-Agent Systems and Agreement Technologies (pp. 277-284). EUMAS 2016.
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Abdullahi, I. & Muller, B. (2016). Towards Efficient Verification of Elementary Object Systems. In Proceedings of the 25th International Workshop on Concurrency, Specification and Programming (pp. 86-100). Concurrency, Specification, and Programming (CS&P 2016).SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50306
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Muller, B. & Betts, J. (2014). Engineering MAS - A Device Integration Framework for Smart Home Environments.. Fundamenta Informaticae, 135(4), 341-353.
All Research
Journal Articles
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Rees, C. & Muller, B. (2022). All that glitters is not gold: trustworthy and ethical AI principles. AI and Ethics, 0
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Muller, B. & Betts, J. (2014). Engineering MAS - A Device Integration Framework for Smart Home Environments.. Fundamenta Informaticae, 135(4), 341-353.
Conference Contributions
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Kovvuri, V., Liu, S., Seisenberger, M., Fan, X., Muller, B., & Fu, H. (2022). On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study. In 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-8). IEEE.
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Williams, S., Ware, J., Müller, B., & Muller, B. (2019). Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia. In Artificial Intelligence in Health-47). Springer Nature Switzerland AG.
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Williams, S., Ware, J., & Muller, B. (2018). Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity. In Proceedings of the First Joint Workshop on AI in Health organized as part of the Federated AI Meeting (FAIM 2018), co-located with AAMAS 2018, ICML 2018, IJCAI 2018 and ICCBR 2018 (pp. 212-223). AIH 2018 - Joint Workshop on AI in Health.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa43607
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Williams, S., Müller, B., & Muller, B. (2017). Agents and Dementia — Smart Risk Assessment. In Multi-Agent Systems and Agreement Technologies (pp. 277-284). EUMAS 2016.
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Abdullahi, I. & Muller, B. (2016). Towards Efficient Verification of Elementary Object Systems. In Proceedings of the 25th International Workshop on Concurrency, Specification and Programming (pp. 86-100). Concurrency, Specification, and Programming (CS&P 2016).SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50306
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Muller, B. (n.d.) The Future of Everything is Distributed – A Plea for Reliable, Responsible, and Resilient Systems Engineering. In IDC 2023, The 16th International Symposium on Intelligent Distributed Computing, Hamburg University of Applied Sciences, Hamburg, Germany, September 13-15 2023Springer.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64472
Supervision
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Untitled (current)
PhDOther supervisor: Dr Alma Rahat -
Untitled (current)
PhDOther supervisor: Dr Nicholas Micallef -
The Extreme Right, Online Propaganda, and Hybrid Human-Automated Content Removal (current)
PhDOther supervisor: Dr Lella Nouri -
Explainable Artificial Intelligence across Domains: Refinement of SHAP and Practical Applications (current)
PhDOther supervisor: Dr Monika Seisenberger
Taught Modules
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CS-265 Artificial Intelligence
CS-265 is an introduction to Artificial Intelligence (AI) focusing primarily on reasoning and problem solving as a search for a solution. The central notions of a rational agent and multi-agent systems are used to introduce concepts like beliefs, desires, and intentions that are fundamental to the search for solutions within the context of an observable environment. Further aspects of symbolic AI such as knowledge representation and expert systems, planning, and language processing will be covered. All of these topics are embedded into a human-centred perspective of AI.
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CSC325 Artificial Intelligence
CSC325 is an introduction to Artificial Intelligence, focusing primarily on reasoning and problem solving as a search for a solution rather than on statistical techniques for classification. The course may cover topics from amongst: search techniques; knowledge representation and expert systems; planning; scheduling; qualitative reasoning; language processing with grammar rules; and meta-programming, as well as agents, multi-agent systems, and agent collaboration.
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CSCM08 Information Security Management
This module will address the theory and practice of information security. In particular, it will consider where data comes from, who collects it and what they can do with it. It will further look into theories of monitoring and surveillance, digital identity, legal and regulatory frameworks, data protection, cybercrime, business resilience, disaster recovery, and security audits.
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CSCM23 Designing-in Trust, Understanding and Negotiation
This module explores state-of-the-art methods and concepts to assist responsible design and development of technology with the aim of creating reliable and trusted systems. The content of this module will be delivered by expert lecturers and practitioners in the areas of trusted computation, bias and explainability in automated decision making and decision support, ethical considerations for AI, argumentation and negotiation, as well as formal methods, such as verification of critical systems.