About Us

What is TAIAO?

Led by the University of Waikato, TAIAO is a data science programme of $13 million (GST exclusive) over seven years, funded by the Ministry of Business, Innovation, and Employment (MBIE). It will advance the state-of-the-art in environmental data science by developing new machine learning methods for time series and data streams that are able to deal with large quantities of big data in real-time, which are tailored to deal with data collected on the New Zealand environment. It will build a new open-source framework to implement machine learning on time series data, provide an open available repository with datasets to improve reproducibility in environmental data science, and build capability in fundamental and applied data science, accessible to all New Zealanders.

This programme is a new collaboration between the Universities of Waikato, Auckland, Wellington and Canterbury, Beca and MetService and includes world-leading data scientists, data engineers, and environmental scientists.

Our Vision

Our vision is to enable the next level of data science to provide robust and fit-for-purpose tools and methods that are accessible and useful to researchers and practitioners across all areas of the New Zealand environment. Our method is to codesign our work with iwi, industry and government, ensuring through pertinent environmental case studies that we maximise benefit, uptake and suitability. Through our training programme, we will enable the next generation of New Zealanders to play a stronger and more useful role in solving the critical environmental problems that face our country. Our internationally-connected research team will make sure that the latest international advances will be adopted and reinvented for our unique environmental setting, while harnessing the passion of our own data science researchers to preserve our famously beautiful lakes, rivers, forests, estuaries and mountains for future generations.

Our Purpose

Led by the University of Waikato, TAIAO is a data science programme of $13 million (GST exclusive) over seven years, funded by the Ministry of Business, Innovation, and Employment (MBIE). It will advance the state-of-the-art in environmental data science by developing new machine learning methods for time series and data streams that are able to deal with large quantities of big data in real-time, which are tailored to deal with data collected on the New Zealand environment. It will build a new open-source framework to implement machine learning on time series data, provide an open available repository with datasets to improve reproducibility in environmental data science, and build capability in fundamental and applied data science, accessible to all New Zealanders.

Our Goals

Data are essential to research, understand, set policy for and manage New Zealand’s environment, but environmental data presents many challenges that require new data science methods to overcome them, and a substantial increase in the capability of environmental researchers, governors and managers to use data science in their work. This programme will develop those new methods and build the required capability. In particular, we will focus on developing methods to deal with environmental datasets that are collected in large volumes over time, and must therefore be dealt with as streams that are analysed incrementally, as they are measured, rather than as collections of data that can be analysed all at once. These methods will address underlying characteristics of the data that evolve over time (e.g. due to climatic or ecological changes), and data that are collected at a range of time intervals and spatial scales ranging from broadscale satellite images to singlepoint measurements on the ground, in the water or air. The methods we develop will be interpretable and explainable (to help users understand why an algorithm produces some particular output), identify and understand anomalies (to distinguish “normal” from “unusual” measurements) and quantify uncertainty in algorithm output (to help decision-makers understand how confident they can be in conclusions drawn from the data science methods). To deliver the methods we develop in a form that environmental scientists and managers can use, we will build a new open source framework to do machine learning on time series data, and provide an open access repository of environmental datasets to improve reproducibility in environmental data science. Through workshops, undergraduate and postgraduate research projects within the programme, we will build New Zealand’s capability in fundamental and applied data science relevant to environmental data, from introductory to postdoctoral level.

Who are we?
Project Partners
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Meet the team

Prof. Albert Bifet, Project Leader, AI Institute, University of Waikato
Prof. Karin Bryan, School of Environment, University of Auckland
Prof. Bernhard Pfahringer, AI Institute, University of Waikato
Prof. Eibe Frank, AI Institute, University of Waikato
Dr. Phil Mourot, Senior Research Fellow, University of Waikato and Waikato Regional Council
Assoc. Prof. Te Taka Keegan, AI Institute, University of Waikato
Prof. Geoff Holmes, Pro Vice Chancellor - Health, Engineering, Computing, and Science, University of Waikato

Dr. Yun Sing Koh, School of Computer Science, The University of Auckland
Dr. Joerg Wicker, School of Computer Science, The University of Auckland
Dr. Varvara Vetrova, College of Engineering, University of Canterbury
Dr. Heitor Murilo Gomes, School of Engineering and Computer Science, Victoria University of Wellington

Dr. Nick Lim, Postdoctoral Fellow, AI Institute, University of Waikato
Dr. Guilherme Weigert Cassales, Postdoctoral Fellow, AI Institute, University of Waikato
Dr. Anany Dwivedi, Postdoctoral Fellow, AI Institute, University of Waikato
Dr. Yaqian Zhang, Postdoctoral Fellow, AI Institute, University of Waikato
Mr. Peter Reutemann, Senior Research Programmer, Department of Computer Science, University of Waikato
Corey Sterling, Research Programmer, AI Institute, University of Waikato

Gregory Pearson, Meteorological Design Engineer, MetService

Orlando Kootstra, Manager - Digital Services, Beca Group Limited
Monica Daniel, Senior Project Manager, Beca Group Limited
Stephen Witherden, Beca Group Limited
Megan Taylor-Silva, Senior Communications and Engagement Advisor, Beca Group Limited.

Dr. Moritz Lehmann, Senior Scientist, Xerra
Christopher McBride, Senior Research Officer, Environmental Research Institute, University of Waikato