Professor Geoff Holmes
Pro Vice Chancellor- Health, Engineering, Computing, and Science
Qualifications: BSc(Hons) PhD S'ton
Contact Details
Email: geoff@waikato.ac.nz
Room: FG.1.01
Phone: +64 7 838 4405
Extension: 4405
About Geoff
I received my degrees in Mathematics from the University of Southampton, England. My PhD involved the development of software packages to assist Mathematicians in solving Einstein's field equations in General Relativity. This was how I got started in Computer Science. After graduating I became a Research Assistant at the Electrical Engineering Department of Cambridge University, England where I was a member of large team working on a speech understanding system. I took up a position as Lecturer in Computer Science in 1987. In 1993 I was appointed Senior Lecturer.
Research Interests
My research interests are fairly broad. I have always held an interest in Computer Speech and have supervised several projects at Waikato on that topic, in particular, speech recognition and speech compression. I currently have two PhD students working on speech compression. I am part of the Department's Machine Learning group where I concentrate my efforts on the application of Machine Learning to agricultural domains. Through my interests in Machine Learning I have recently become very interested in the concept of knowledge discovery in databases.
Recent Publications
Holmes, G., Frank, E., Fletcher, D., & Sterling, C. (2022). Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models. In Proc 27th International Conference on Intelligent User Interfaces (IUI '22) (pp. 584-593). New York, NY, USA: ACM. doi:10.1145/3490099.3511110
Mitchell, R., Cooper, J., Frank, E., & Holmes, G. (2022). Sampling permutations for Shapley value estimation. Journal of Machine Learning Research, 23(43), 1-46. Retrieved from https://jmlr.org/papers/volume23/21-0439/21-0439.pdf Open Access version: https://hdl.handle.net/10289/14742
Wang, H., Frank, E., Pfahringer, B., Mayo, M., & Holmes, G. (2022). Cross-domain Few-shot Meta-learning Using Stacking. arXiv. Retrieved from http://arxiv.org/abs/2205.05831v1
Mitchell, R., Frank, E., & Holmes, G. (2022). GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles. PeerJ Computer Science, 8, e880. doi:10.7717/peerj-cs.880 Open Access version: https://hdl.handle.net/10289/14815
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