Ihaka Lecture Series
In March 2017 the Department of Statistics launched a new, annual lecture series named after Associate Professor Ross Ihaka in honour of his contributions to the field. Find out about the 2020 lecture series below.
Ross Ihaka, along with Robert Gentleman, co-created R – a statistical programming language now used by the majority of the world’s practising statisticians. It is hard to over-emphasise the importance of Ross’s contribution to our field. We named this lecture series in his honour to recognise his work and contributions to our field in perpetuity.
Find out more about Ross Ihaka here
The role of statistics and computing in public and social policy
Corporations are collecting and mining mountains of data to make better consumers of us all, but there are also vast quantities of data being gathered by public organisations for administrative and policy purposes.
The 2020 Ihaka Lecture Series brings together three experts to discuss the challenges and rewards of applying data science to societal issues.
Our thanks to The New Zealand Statistical Association who are our official sponsors for the 2020 Ihaka Lecture Series.
The triumph of the quants?: Model-based poll aggregation for election forecasting
Professor Simon Jackman, Chief Executive Officer at the United States Studies Centre, will examine recent successes and failures of predictive models of election outcomes. Professor Jackman will also discuss trends and discontinuities in the evolution of public opinion over election campaigns, spatial smoothing and pollster biases.
Professor Jackman's current research focuses on opportunities and challenges of web-based survey research, consequences of under-representation in social research, and developing methodologies for assessing symmetry and fairness.
Machine learning for causal inference: Magic elixir or fool’s gold?
Professor Jennifer Hill from New York University will review the conceptual issues involved in understanding causal mechanisms and describe the potential for machine learning to improve our understanding of these mechanisms.
Professor Hill develops and evaluates methods that help us answer the causal questions that are vital to policy research and scientific development. Methodologically, she focuses on methods for situations where it is difficult or impossible to perform traditional randomized experiments. In her applied work, Professor Hill focuses on applications of randomized experiments to policy and practice and on using machine learning to generate causal hypotheses.
**Please note this event has now been cancelled**
Lecture 3 | 25 March 2020 (cancelled)
Implementing a machine learning tool to support high-stake decisions in child welfare: A case study in human centred AI
Professor Rhema Vaithianathan, from the Centre for Social Data Analytics at AUT, will reflect on what we can learn about applying data analytics in a trusted way, covering key concepts like consent, transparency, fairness and community voice, and how they can contribute to project success or failure.
Professor Vaithianathan will talk about emerging ‘rules of engagement’ for social good uses of data analytics, drawing on her experience 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.
Rise of the machine learners: Statistical learning in the computational era
Whether labelled as machine learning, predictive algorithms, statistical learning, or AI, the ability of computers to make real-world decisions is rising every year.
The 2019 Ihaka Lecture Series brought together four experts at the interface of statistics and computer science to discuss how computers do it, and how much we should let them.
Our thanks to The New Zealand Statistical Association who are our official sponsors for the 2019 Ihaka Lecture Series.
Open source Machine Learning @ Waikato
Professor Bernhard Pfahringer from the Machine Learning research group at the University of Waikato discusses open-source Machine Learning software suites. He reflects on their design and their position in the current international Machine Learning landscape.
Deep learning: why is it deep, and what is it learning?
University of Auckland Professor Thomas Lumley discusses the rise of neural networks. He provides insight into how deep convolutional nets are structured and how they can be effective, but also why they are brittle and can fail in remarkably alien ways.
Algorithmic fairness: Examples from predictive models for criminal justice
Dr Kristian Lum from the Human Rights Data Analysis Group discusses the use of predictive models in the criminal justice system. Using examples from predictive policing and recidivism risk assessment she demonstrates how such models could perpetuate and potentially amplify data-encoded biases.
Statistical learning and sparsity
Professor Robert Tibshirani from Stanford University reviews the lasso method for high dimensional supervised learning and discusses some new developments in the area, including the Pliable Lasso, and post-selection inference for understanding the important features.
A thousand words: Visualising statistical data
A picture is worth a thousand words – or perhaps that should be a million numbers. The distillation of data into an honest and compelling graphic is an essential component of modern (data) science.
The 2018 Ihaka Lecture Series displayed the contributions of three experts across different facets of data visualisation.
Myth-busting and apophenia in data visualisation: Is what you see really there?
Plots of data are important tools for observing patterns, but it is easy to imagine patterns that may not exist. Using two protocols the Rorschach and the lineup, Professor Dianne Cook of Monash University describes some simple tools for helping to decide if patterns are real.
Making colour accessible
University of Auckland Associate Professor Paul Murrell investigates the 'BrailleR' package for R and its difficulties with colour. By making a mountain out of that molehill, Paul embarks on a daring Statistical Graphics journey featuring colour spaces, high-performance computing, Te Reo, and XKCD.
Visual trumpery: How charts lie – and how they make us smarter
With facts and truth increasingly under assault, the use of graphs, charts, maps and infographics have become popular in supporting all manner of spin. Identifying information from misinformation is an important skill for any citizen. Alberto Cairo from the University of Miami teaches some guiding principles on how people can become more critical and better-informed readers of charts.
Statistical Computing in the Data Age
Statistics has become essential in the data age. We have an increasing ability to collect vast quantities of data, but often still struggle to make sense of it.
The 2017 Ihaka lectures aimed to highlight the important role that both statistics and computing play in this endeavour.
Expressing yourself with R
Hadley Wickham Chief Scientist at RStudio discusses Expressing yourself with R.
R and data journalism in New Zealand
Harkanwal Singh Data Editor from the New Zealand Herald on the use of R in New Zealand's data journalism landscape.
Interactive visualisation and fast computation of the solution path for convex clustering and biclustering
Genevera Allen, from Dobelman Family Junior Chair and Departments of Statistics and Electrical and Computer Engineering at Rice University, discusses clustering as a fundamental tool for exploratory analysis of big data.
Statistical computing in a (more) static environment
Ross Ihaka Associate Professor in the Department of Statistics at the University of Auckland discusses the spectrum of statistical computing systems from the dynamic to the very static.