Dr Michael Mayo

Senior Lecturer (Computer Science)

Qualifications: BA(Hons) Otago PhD Cant

Contact Details

Room: G.2.24
Phone: +64 7 838 4403
Extension: 4403

Research Interests

Artificial intelligence (machine learning, evolutionary algorithms etc) and health/medicine. Some recent conference presentations: CGMs/privacy, improved Glucose Variability Percent metric.

Teaching Commitments

Recent Publications

  • Podolskiy, V., Mayo, M., Koay, A., Gerndt, M., & Patros, P. (2019). Maintaining SLOs of cloud-native applications via self-adaptive resource sharing. In Proc 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) (pp. 72-81). Umeå, Sweden: IEEE. doi:10.1109/SASO.2019.00018

  • Daoud, M., & Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine, 97, 204-214. doi:10.1016/j.artmed.2019.01.006 Open Access version:

  • Mayo, M., & Daoud, M. (2019). Data normalisation using differential evolution and aggregated logistic functions. In Proc 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019) (pp. 920-927). Wellington, NZ. doi:10.1109/CEC.2019.8790251

  • Daoud, M., Mayo, M., & Cunningham, S. J. (2019). RBFA: Radial Basis Function Autoencoders. In 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019) (pp. 2966-2973). Wellington, NZ. doi:10.1109/CEC.2019.8790041

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