Professor Geoff Holmes
Dean of Computing & Mathematical Sciences
Qualifications: BSc(Hons) PhD S'ton
Phone: +64 7 838 4405
Fax: +64 7 838 4155
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.
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.
Bifet, A., Gavaldà, R., Pfahringer, B., & Holmes, G. (2018). Machine learning for data streams with practical examples in MOA. MIT Press. Retrieved from https://mitpress.mit.edu/books/machine-learning-data-streams
Gibert, K., Horsburgh, J. S., Athanasiadis, I. N., & Holmes, G. (2018). Preface to the thematic issue on Environmental Data Science. Applications to air quality and water cycle. Environmental Modelling and Software, 106, 1-3. doi:10.1016/j.envsoft.2018.03.020
Bifet, A., Read, J., Holmes, G., & Pfahringer, B. (2018). Streaming data mining with Massive Online Analytics (MOA). In M. Last, H. Bunke, & A. Kandel (Eds.), Data Mining in Time Series and Streaming Databases (pp. 1-25).
Holmes, G., Liu, T. Y., Li, H., King, I., Sugiyama, M., & Zhou, Z. H. (2017). Introduction: Special Issue of Selected Papers from ACML 2015. Machine Learning, 106(4), 459-461. doi:10.1007/s10994-017-5636-6 Open Access version: https://hdl.handle.net/10289/11057
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