Ihaka Lecture Series 2026

Brought to you by the Faculty of Science's Department of Statistics, the 2026 Ihaka Lecture Series theme is "Thinking Critically, Creatively, and Responsibly with Data".

Overview:
The speakers this year bring three different lenses to data analysis, to focus on aspects that we often ignore. Across the talks, we examine what it means to build trust in data visualisation, how creative experimentation can deepen our understanding of analytical tools like R, and why paying attention to anomalies and outliers can reveal important stories that would otherwise be forgotten. Together, the talks highlight data analysis as both a technical and human endeavour, calling for principled methods, thoughtful design, and curiosity about the unexpected. The series invites attendees to reflect on how statistical practice shapes understanding, communication, and trust in a data-driven world.

For more information, please see the Ihaka Lecture Series webpage.

Thursday 23 July 2026
Talk title: Trustworthy Data Visualization

Speaker: Kieran Healy, Professor of Sociology, Duke University, U.S.A.

Abstract

Visualizations are the most widespread, the most accessible, and in some ways the most authoritative-looking way to present data to audiences. In this talk I ask: where does trust in data come from? Can software like R help us be trustworthy? And what are its limits, when it's easier than ever to do a lot with data, including maybe a lot of untrustworthy things?

Thursday 30 July 2026
Talk title: When Creativity Meets Code

Speaker: Dr Nicola Rennie, Data visualisation specialist, Office for National Statistics

Abstract

When we think of R, it’s often as a practical tool for analysis, reporting, and reproducible research. But it can also be a space for creativity, which encourages experimentation, curiosity, and a different way of engaging with data. In this talk, we’ll explore how using R as a creative outlet (whether through treating data visualisation as a design process, developing generative art systems, or simply building "useless" things) can deepen our understanding of the tools we use every day. By stepping outside the usual workflows, we'll uncover new ways to think about visualisation, design, and storytelling with data.

Along the way, we’ll reflect on how these creative practices translate back into day-to-day statistical work, helping us build clearer, more engaging narratives and more thoughtful visualisations. The aim isn’t to turn everyone into an artist, but to show how a creative mindset can make us more effective communicators and more confident users of R.

Thursday 8 August 2026
Talk title: Spotting the Weird Ones

Speaker: Professor Rob J Hyndman, Monash University, Australia

Abstract

Data analysis is about finding the stories hidden in the mass of information that make up a data set. Usually we are interested in understanding the major patterns and the relationships that hold true for most of the data. But sometimes we need to look at the weird observations, the mavericks, the ones that don't follow the crowd and behave differently. These observations are called 'anomalies'. Many anomaly detection methods have been developed, but they are often based on ad hoc rules and hidden assumptions, and can lead to misleading conclusions.

Instead, I will describe a principled statistical approach to identifying anomalies in diverse data sets, from simple numerical data to complex high-dimensional data objects. The ideas will be illustrated using the {weird} package for R applied to several data analysis problems, including spotting over-priced wines, identifying errors in the records of the Old Faithful Geyser, and uncovering forgotten epidemics in 19th century France.

Info: Light canapes and finger-food will be served prior to the lecture