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Department of Statistics

Ihaka Lecture Series

In March 2017 the Department of Statistics launches a new, annual lecture series named after Associate Professor Ross Ihaka in honour of his contributions to the field.

The Ihaka lectures

The series is named after Ross Ihaka, Associate Professor in the Department of Statistics at the University of Auckland. Ross, along with Robert Gentleman, co-created R – a statistical programming language now used by the majority of the world’s practicing 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.

Our debut series has the theme Statistical Computing in the Data Age and will run 8-29 March, 2017. We will be streaming the lectures live for those who cannot attend an event If you cannot attend. Find the details on each event page below.


Statistical Computing in the Data Age

Statistics has become essential in the data age. We have increasing ability to collect vast quantities of data, but often still struggle to make sense of it. The Ihaka lectures aim to highlight the important role that both statistics and computing play in this endeavour.


2017 schedule

Lectures commence at 6.30pm, Wednesdays, MLT1 Lecture Theatre, Ground Floor, Building 303, 38 Princes Street.

Refreshments will be available before each lecture at 6pm in the foyer area of Building 302, 23 Symonds Street.

See our campus map for location.

We will be streaming the lectures live for those who cannot attend an event If you cannot attend. Find the details on each event page below.


Photograph of Hadley Wickham

Wednesday 8 March – Expressing yourself with R

Hadley Wickham 
Chief Scientist, RStudio
Associate Professor, Department of Statistics, University of Auckland

Photograph of Harkanwal Singh

Wednesday 15 March – R and data journalism in New Zealand

Harkanwal Singh
Data Editor, New Zealand Herald

Photograph of Genevera Allen

Wednesday 22 March – Interactive visualisation and fast computation of the solution path for convex clustering and biclustering

Genevera Allen
Dobelman Family Junior Chair
Departments of Statistics and Electrical and Computer Engineering, Rice University

Photograph of Ross Ihaka

Wednesday 29 March – Statistical computing in a (more) static environment

Ross Ihaka
Associate Professor, Department of Statistics, University of Auckland


Ross Ihaka

Photograph of Associate Professor Ross Ihaka
Associate Professor Ross Ihaka

Associate Professor Ross Ihaka was born in South Auckland and received his education in a variety of small country schools in the Cook Islands and the northern North Island. He completed his BSc(Hons) and MSc Mathematics degrees at the University of Auckland before undertaking his doctoral study in statistical aspects of seismology at the University of California at Berkeley. After completing his PhD he held positions at Yale University and the Massachusetts Institute of Technology before taking up his current position at the University of Auckland.

Associate Professor Ihaka’s first experience with computing was in an Applied Mathematics course taught by Garry Tee, John Butcher and F D K Roberts. His next serious exposure to computing was in a group of Berkeley students working on a local variant of the Princeton ISP system under the guidance of Jim Reeds. He also learned about computational statistics and graphics under the guidance of Dan Asimov, Leo Briemann, David Brillinger, Louis Jaekal and Jim Reeds.

During his student career, Associate Professor Ihaka honed his computing skills by developing two of his own interactive systems for statistical computing and added a further two soon after graduating (with some input from John Hartigan). At the University of Auckland he developed the R statistical system with Robert Gentleman. Initially R was a platform for computing experiments, but it was given a superficial veneer to make it look like the Bell Laboratories’ S system. The rationale for imposing this similarity was to get access to the large amount of ‘free’ code available for S. The R system has gone on to achieve significant success.

R is now widely used all over the world, and Associate Professor Ross Ihaka continues to work on various other statistical computing systems.