About the AI Institute

We aim to encourage greater connection and knowledge-sharing between tertiary and wider research organisations and industry to support the development of a vibrant AI ecosystem in New Zealand.  The Institute has a special focus on real-time analytics for big data, machine learning and deep learning.

The establishment of the Institute is the culmination of many years of leading AI research and teaching led by staff at the University of Waikato, including the development of some of the most popular open source tools in the world  (WEKA, MOA, and ADAMS) which have been downloaded more than 12 million times.  Our researchers have also published books which are used by many universities across the world to support their teaching.

Read more about our software.

Inspired by their experience at Waikato, many Waikato graduates have gone on to achieve amazing things including Dr Shane Legg, the co-founder of Google DeepMind and Dr Craig Nevill-Manning who founded Google’s first remote engineering center located in New York.

Our Name and Logo

E ai ki a Pei Te Hurinui o Waikato-Maniapoto, nā Tāwhaki ngā toi huarewa i kakea ai, ka riro māi ai ngā Kete o te Mātauranga, ngā Whatu Kura, me te hiringa o te Mahara. Heria ai e ia he ipu wai. Ngēngē ana, hiainu ana, nā te waiora mai i aua ipu i whai ora, i whai ngoi ai a ia. Koia nei te tikanga o te ingoa nei, Te Ipu o te Mahara. Kei te whakairo nei te tohu Poutama, ngā pū raraunga, me the piki ake i a Papatuanuku (kākāriki) ki a Ranginui (kikorangi).

According to Waikato-Maniapoto lore it was Tāwhaki who ascended into the heavens to obtain the sacred baskets of knowledge, the stones of consolidation, and thus of the depth and breadth of thought (Mahara). On his endeavours he became weary and fatigued so he turned to the gourds (Ipu) for sustenance and strength. Hence the meaning of Te Ipu o te Mahara. The symbol has elements of the Poutama design; convergences of data points; and the ascendance from the earth (green) to the sky (blue).

Research excellence

Work collaboratively to address factors limiting AI research in New Zealand, including talent, funding, and research focused opportunities, incentives and infrastructure, so that a sustainable research ecosystem may thrive.

Connect, network and collaborate

Connect researchers, develop strong networks between researchers and with stakeholders, and actively promote and facilitate opportunities to develop impactful collaborations.

Translation and implementation

Support the translation and implementation of research outputs across various stakeholder groups for the benefit of industry, as well as our communities and the environment.

Impact and awareness

Curate, collate and share stories of impact and lead the delivery of programmes and initiatives to raise awareness and provide opportunities for the wider community to engage.

Albert Bifet - Director

Albert previously worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, and UPC BarcelonaTech. He is the co-author of a book on Machine Learning from Data Streams published at MIT Press. He is one of the leaders of MOA, scikit-multiflow and Apache SAMOA for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of KDD BigMine (2019-2012), and ACM SAC Data Streams Track (2021-2012).

Te Taka Keegan - Co-Director for Māori

Te Taka has worked on a number of projects involving the Māori language and technology including the Māori Niupepa Collection, Te Kete Ipurangi, the Microsoft keyboard, Microsoft Windows and Microsoft Office in Māori, Moodle in Māori, Google Web Search in Māori, and the Māori macroniser. In 2009 Te Taka spent 6 months with Google in Mountain View as a visiting scientist assisting with the Google Translator Toolkit for Māori. Further work with Google led to Translate in Māori. Te Taka’s general research interests include traditional navigation, Māori language technologies, indigenous language interfaces, and multi-lingual usability. His current research interests have focused on the use of Te reo Māori in a technological environment.

In 2013, Te Taka was awarded the University of Waikato's Māori/Indigenous Excellence Award for Research and in 2017 Te Taka was awarded the Prime Minister’s Supreme Award for Tertiary Teaching Excellence.

Bernhard Pfahringer - Co Director

Bernhard received his PhD degree from the University of Technology in Vienna, Austria, in 1995. He is a Professor with the Department of Computer Science at the University of Waikato. His interests span a range of data mining and machine learning sub-fields, with a focus on streaming, randomization, and complex data.

Eibe Frank - Co-Director

Eibe obtained a first degree in computer science from the University of Karlsruhe, Germany, and a PhD in computer science from the University of Waikato. He has published extensively in the areas of machine learning and data mining and refereed for many conferences and journals in these areas.

Jointly with others, he has received a Service Award from the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining and two Test of Time Awards from the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.