Projects per year
Personal profile
Research interests
- AI for Extremes (Flooding, Rainfall, Drought etc.)
- Machine Learning Operations (Governance, Pipelines, Monitoring & Deployment)
- Anomaly and Normality Detection
- Data Engineering (Distribution shift and skew)
- Societal Issues (e.g Digital Divide)
Teaching interests
- Machine Learning
- Databases and Data Systems
- Operational Machine Learning
- Ethical, legistlative and societal factors in AI/technology.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Machine Learning, Doctor of Philosophy, Machine Learning methods for the analysis of precipitation patterns
Computer Science, Bachelor of Science, Genetic optimisations for satisfiability and Ramsey theory, University of Plymouth
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Developing rainfall frequency models for critical infrastructure design in a changing environment
Kjeldsen, T. (PI) & Barnes, A. (CoI)
1/08/24 → 31/07/26
Project: Central government, health and local authorities
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CCTV Image-based classification of blocked trash screens
Cornelius Smith, R., Barnes, A., Wang , J., Dooley, S., Rowlatt, C. & Kjeldsen, T., 28 Aug 2024, (Acceptance date) In: Journal of Flood Risk Management.Research output: Contribution to journal › Article › peer-review
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Identifying the pathways of Extreme Rainfall in South Africa Using Storm Trajectory Analysis and Unsupervised Machine Learning Techniques
Phillips, R., Johnson, K., Barnes, A. P. & Kjeldsen, T., 1 Jan 2024, In: Hydroinformatics. 26, 1, p. 162-174Research output: Contribution to journal › Article › peer-review
Open Access -
Machine learning approaches for anomalous storm pattern identification
Sharp, D. & Barnes, A. P., 30 Apr 2024, In: Hydroinformatics. 26, 4, p. 819–834 16 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Novel approach to quantifying long-term rainfall distribution variation
Barnes, A. & Stamataki, I., 2024.Research output: Contribution to conference › Abstract › peer-review
Open Access -
Forecasting seasonal to sub-seasonal rainfall in Great Britain using convolutional-neural networks
Barnes, A., McCullen, N. & Kjeldsen, T., 31 Jan 2023, In: Theoretical and Applied Climatology. 151, 1-2, p. 421-432 12 p.Research output: Contribution to journal › Article › peer-review
Open Access4 Citations (SciVal)
Thesis
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Machine learning methods for the analysis of precipitation patterns: (Alternative Format Thesis)
Barnes, A. (Author)Kjeldsen, T. (Supervisor), Prosdocimi, I. (Supervisor) & McCullen, N. (Supervisor), 23 Mar 2022Student thesis: Doctoral Thesis › PhD
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Prizes
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Most Engaging Lecturer (Meme Supreme)
Barnes, A. (Recipient), 2024
Prize: Prize (including medals and awards)
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Mary Tasker (Teaching Excellence)
Barnes, A. (Nominee), 2024
Prize: Prize (including medals and awards)
Activities
- 1 Visiting lecturer
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Royal Agricultural University
Barnes, A. (Visiting lecturer)
1 Feb 2024 → …Activity: Visiting an external academic institution › Visiting lecturer