Dr Michael Mayo

Senior Lecturer (Computer Science)

Qualifications: BA(Hons) Otago PhD Cant

Contact Details

Room: G.2.20
Phone: +64 7 838 4403
Extension: 4403
Fax: +64 7 858 5095

Research Interests

evolutionary algorithms, deep learning, evolutionary machine learning, artificial intelligence and medicine/health, energy optimisation

Teaching Commitments

Recent Publications

  • Mayo, M., & Daoud, M. (2017). Aesthetic local search of wind farm layouts. Information, 8(2), 39. doi:10.3390/info8020039 Open Access version:

  • Mayo, M., & Goltz, N. (2017). Constructing document vectors using kernel density estimates. In V. Torra, Y. Narukawa, A. Honda, & S. Inoue (Eds.), Modeling Decisions for Artificial Intelligence. MDAI 2017 (pp. 183-194). Cham: Springer. doi:10.1007/978-3-319-67422-3_16

  • Goltz, N., & Mayo, M. (2017). Enhancing Regulatory Compliance by Using Artificial Intelligence Text Mining to Identify Penalty Clauses in Legislation. In MIREL 2017 - Workshop on `MIning and REasoning with Legal texts', held in conjunction with the 16th International Conference on Artificial Intelligence and Law. Conference held at King’s College, London, UK.

  • Wilson, B., Wakes, S., & Mayo, M. (2017). Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning. In Proc 2017 IEEE Symposium Series on Computational Intelligence (pp. 1-8). Honolulu, Hawaii: IEEE. doi:10.1109/SSCI.2017.8280844 Open Access version:

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