2021 series – Looking on the bright side


We should be worried about how much of our personal data businesses are gathering, but are there benefits to be had from allowing our health system to know more about us? We are on constant guard to protect our computers from viruses, but when a virus strikes humanity, can our computers help to protect us? We know that giving teenagers the ability to communicate 24/7 can have negative outcomes, but what happens when scientists get hold of social media tools?

The 2021 Ihaka Lecture Series features three speakers who will describe how modern computing can be used to positively impact the world.

All lectures are at University of Auckland Conference Centre,Building 423 (423-342) or they can be watched via LIVESTREAM.


Lecture 1: Thursday 29 July 2021, 6:30pm

Data Science in the Connected Era

Dr Simon Urbanek Senior Lecturer, Department of Statistics, University of Auckland

Our world is increasingly interconnected, which has several implications. On the one hand it increases the amount and variety of data we can collect to make informed decisions and improve our lives, but also it allows us to perform data analyses without constraints related to the physical location of the data or compute infrastructure.

Modern computer technologies such as cloud computing and the Web have given rise to social media, but in this talk we will explore the possibilities of leveraging them for visualisation and data analysis, connecting people with data across the world and fostering collaboration.

We will illustrate the benefits of that approach using RCloud – a collaborative tool for data analysis and interactive visualisation which supports several data analytic languages, distributed computing, discovery, sharing and reproducible research. It allows us to analyse data collaboratively at a large scale and communicate results efficiently.

Professor Simon Urbanek is a Senior Lecturer in the Department of Statistics at the University of Auckland. Simon obtained his PhD in Statistics from the Augsburg University, Germany in 2004 and has worked at AT&T Labs in Data Science and AI Research for 15 years, leading research and projects on large-scale data analysis in the areas of mobility networks, TV and advertising.

His main interests are visualisation, interactive graphics, big data analytics, statistical and distributed computing. He is member of the R Core Development Team and author of numerous popular R packages including Rserve, multicore, rJava, iPlots, RJDBC and iotools.


Lecture 2: Thursday 5 August 2021, 6:30pm

Implementing a Machine-Learning Tool to Support High-Stakes Decisions in Child Welfare: A case study in Human Centred AI

Professor Rhema Vaithianathan, Centre for Social Data Analytics, AUT

Data analytics techniques like predictive risk modelling offer incredible opportunities to learn from rich data sets and make decisions supported by data. But while the private sector has been quick to realise the benefits of data analytics (especially as a tool to drive profitability), the public sector has moved much slower, despite needing new solutions to many wicked social problems.

Professor Rhema Vaithianathan will reflect on what we can learn about applying data analytics in a trusted way, from the very different experiences of the private and public sectors. In particular, she will talk about different approaches to key concepts like consent, transparency, fairness and community voice and how they can contribute to project success or failure. She will go on to talk about new ‘rules of engagement’ that are emerging for social good uses of data analytics, drawing on her experiences implementing the Allegheny Family Screening Tool, a machine learning tool used to support screening of child abuse calls in Allegheny County, PA (United States) since 2016, and scaling out of this work in California and Colorado.

Professor Vaithianathan is a Professor of Economics at Auckland University of Technology where she is director of the Centre for Social Data Analytics, a research centre focused on using data analytics for social impact. She is also a Professor of Social Data Analytics at the Institute for Social Science Research at The University of Queensland, where she leads a second node of the Centre
for Social Data Analytics.


Lecture 3: Thursday 12 August 2021, 6:30pm

Modelling to support the COVID-19 response in Aotearoa New Zealand

Dr Rachelle Binny, Manaaki Whenua-Landcare Research and Te Pūnaha Matatini

Mathematical models are playing an important role in the ongoing pandemic, providing insights into the spread of the virus and the effects of interventions to help inform response strategies. This seminar will give an overview of mathematical modelling by Te Pūnaha Matatini to support New Zealand’s COVID-19 response. We will describe the models used to simulate spread of COVID-19 in New Zealand, how they can help inform decisions on switching between Alert Levels, and how we are modelling the risk of new cases arriving at the border.

Rachelle Binny is a mathematical biology researcher at Manaaki Whenua – Landcare Research in Christchurch NZ, and a Principle Investigator in Te Pūnaha Matatini, the NZ Centre of Research Excellence for Complex Systems and Networks. Her research lies at the interface of mathematics, statistics and biology and is data-driven. Following a BSc in Mathematical Biology (University of Dundee, Scotland), she undertook a PhD (University of Canterbury, Christchurch) to develop new models of collective cell behaviour in wound healing, and calibrate these using experimental data. After completing her PhD in 2015, she spent two years as a postdoc at Manaaki Whenua (a Crown Research Institute for environment and biodiversity) before taking on a Researcher position there. Rachelle’s current research combines modelling theory with data from ecological systems to guide conservation management.