Upskilling researchers in Machine Learning
Maxime Rio2,3 Jens Brinkmann1 1University of Auckland, New Zealand 2New Zealand eScience Infrastructure (NeSI) New Zealand 3National Institute of Water and Atmospheric research (NIWA) New Zealand
Abstract
Emerging tools and techniques in the space of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are set to change many aspects of our daily lives and research is no exception. But how do we ensure that researchers are equipped to understand and utilise these tools and techniques across the vast spectrum of research domains?
In this talk we discuss ongoing efforts in Aotearoa/New Zealand to introduce and train researchers in ML and DL, and will touching upon the following topics: developing content and training materials; challenges in teaching and facilitating these techniques; reaching, teaching, and engaging with a broad audience; encouraging the growing community of practice; and providing support for learners and practitioners through compute and other services.
Biography
The panellists of this BoF represent a range of universities, national institutes, and not-for-profit companies that are involved in and support research throughout Australasia.
Dr Maxime Rio, Data science engineer at NeSI and a data scientist at NIWA, with a background in probabilistic models, HPC and image analysis.
https://orcid.org/0000-0003-2553-0721, https://www.nesi.org.nz/about-us/who-we-are
Dr Jens Brinkmann, eResearch engagement specialist at the University of Auckland, with a background in engineering and interest in future technologies.
https://orcid.org/0000-0003-4682-0787, https://cer.blogs.auckland.ac.nz/people/