The 21st Australasian Data Science and Machine Learning Conference (AUSDM'23)

Auckland, New Zealand, 11-13 December 2023

Data Science, Machine Learning, and AI Innovation Day

Welcome to Innovation Day!

We are thrilled to invite you to a day dedicated to exploring the exciting advancements in Data Science, Machine Learning, and Artificial Intelligence. This day is designed to foster collaboration, inspire innovation, and provide a platform for networking among professionals and enthusiasts alike. Our day begins with a thought-provoking keynote session. Followed by a Māori Algorithmic Sovereignty Roundtable. Engage with industry leaders in two compelling talks, gain hands-on experience in our tutorial session, and immerse yourself in exciting research paper presentations throughout the day!

Keynote Session:

NAOI – Improving Science by Integrating Strong AI

Michael Witbrock

Michael Witbrock received his BSc(Hons) in Physiological Psychology from Otago University, and his PhD in Computer Science (AI) from Carnegie Mellon University. He previously held positions at Lycos, Cycorp and IBM Research, and currently is a full professor at the University of Auckland, where he leads a research group, the Strong AI Lab, at the intersection of machine learning, reasoning and natural language understanding, with an additional focus on maximising the near-term benefit of AI to Aotearoa/NZ, and more generally achieving the best global social and civilizational impacts of increasingly powerful AI. While maintaining a strong interest in knowledge-capture and natural language understanding, his current research goals involve the development and use of systems that learn to perform careful reasoning and carry out complex tasks. He is the founding director of the NAOInstitute which studies Natural, Artificial and Organisational Intelligence, and how they interact.

Māori Algorithmic Sovereignty Roundtable:

Massive problems need MASov solutions

Roundtable Speakers:

Ben Ritchie
(Nicholson Consulting)
Kiri West
(University of Auckland)
Daniel Wilson
(University of Auckland)
Paul Brown
(University of Waikato)

Industry Talk:

Transitioning from research topic to business solution using project management methods

Scott Spence

The application of Artificial Intelligence has recently undertaken urgency in government and business sectors. To avoid unnecessary expenditure and wasted resources, efforts to implement AI solutions in these projects need experimental environments and careful management. Organisations need to avoid a gold rush mentality that throws the majority of funds at endeavours that are less likely to lead to commercial and societal benefits. For those commercializing Artificial Intelligence projects, we propose applications of project management best practices. This includes the application of project management methods such as agile delivery, defined business cases and curated governance structures. Managing these investments to balance entrepreneurial endeavours and planned outcomes requires structure and organisational adoption of an AI strategic framework. Managing expectations from customers, staff, and management is part of the mix, as is defining core competencies and organisational values. We will propose a roadmap for organisation adoption and items to be considered when integrating with existing project investments. A New Zealand perspective will, in particular, be considered based on past successes and failures by government and private sector organisations.

Industry Talk:

Democratizing Data & AI Technology for a Smarter, More Connected World

Jiamou Liu

Jiamou Liu is a Senior Lecturer at the University of Auckland. He works in the field of artificial intelligence, with a specific interest in multi-agent systems and graph learning. Dr. Liu has written over 120 research publications and his research has been supported by the Marsden Fund. His recent work in data pricing and data marketplaces has led to the establishment of SoverEx, a company dedicated to building a Web3-based data marketplace that enables the secure trading of private data. The company was co-founded by Jiamou Liu along with Louisa Choe (Otago), Alex Zhang (BIT), and Michelle Zhang (Durham). Dr. Liu also serves as the deputy director of the Master of AI program at the University of Auckland, and a local chair for AAMAS 2024, to be held in Auckland in May 2024.
SoverEx is an exchange, built in the Web3 ecosystem, to empower the next generation of Internet users and businesses with enhanced data sovereignty and access to cutting-edge technologies. We envision a world where people have full control over their personal data, can make informed decisions about its use, and are fairly rewarded for sharing it.


Spatial and Complex Network Data Analysis using Julia

Giulio Valentino Dalla Riva

Giulio Valentino Dalla Riva is a Senior Lecturer in Data Science in the School of Mathematics and Statistics, University of Canterbury, and he is the founder of Baffelan – Data Climbing, a boutique data consultancy company. His research explores and tries to make sense of what happens in complex, dynamical networks. He is interested in ecological networks and the evolutionary processes that modify them in time. Giulio develops mathematical and statistical tools to study the relationship between ecological biodiversity and evolutionary diversity. He is also interested in social networks, especially online, and trying to understand what makes them work in the way they work.

This tutorial explores Complex Network and Spatial Data Analysis in Julia, targeting data miners, spatial scientists, network analysts, urban planners, and researchers. Initially, it covers network analysis foundations with Graphs.jl and LightGraphs.jl, advancing to community detection and visualization using CommunityDetection.jl and GraphPlot.jl. Subsequently, it delves into spatial data analysis, elucidating data manipulation and visualization with GeoIO.jl, Makie.jl, and GeoMakie.jl, and exploring spatial statistics with GeoStats.jl. The final segment unveils the interplay between spatial and network data through case studies, emphasizing spatial proximity evaluation and SpatialGraphs.jl for handling geospatially embedded networks. Through hands-on exercises, attendees will gain practical insights into leveraging Julia for complex network and spatial data analysis.

A tiny warmup has been prepared for anybody who would like to attend this tutorial (it makes the experience smoother by already installing some stuff). It can be found here:


Days until Conference








Keydates (AoE)

Abstract submission: 18 Aug 23
Paper submission: 25 Aug 23
Paper notification: 24 Sept 23
Tutorial/Workshop submission: 26 Sept 23
Tutorial/Workshop notification: 29 Sept 23
Camera-ready: 8 Oct 23
Author Registration: 8 Oct 23

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