National Institute for Applied Statistics Research Australia

The National Institute for Applied Statistics Research Australia (NIASRA) is committed to developing and applying innovative statistical methods to important problems. It undertakes a range of fundamental and contract research, major consulting projects, and professional education in statistical methodology. A number of research centres and a consulting centre are contained within NIASRA.

NIASRA’s research in applied statistics and data science is focused on Biometry and Bioinformatics, Environmental Informatics, Sample Survey Methodology, Health and Social Analytics, and Statistical Consulting. The Director of NIASRA is Professor Marijka Batterham. 

Our aim is to provide leading-edge research and consulting capacity in applied statistics for Australia and our region through the skills and activities of our staff and research students. We collaborate extensively with researchers and professionals.


Upcoming events

School of Mathematics and Applied Statistics presents:

2021 Statistical Science Lecture

Register in advance with Karin Karr (

who will advise on attendance options closer to the date


Scott L. Zeger

John C. Malone Professor of Biostatistics and Medicine
The Johns Hopkins University Bloomberg School of Public Health and School of Medicine, Baltimore, USA. 

Date: Wednesday 3 November 2021
Time: 10.45am for 11am
Venue: UOW Building 43, Room G01. Also, delivery by Zoom

Machine Learning Taster

The next meeting of the Data and Decision Science Network will be an introduction to machine learning.

When: 12:00 pm - 1:30 pm, 7 October 2021
Where: zoom (link to follow registration)
Register: register at eventbrite

This is a general presentation for anyone curious about what machine learning is (and isn’t).

Today, new technologies are generating measurements for almost everything with the ability to store these massive amounts of variables and data. There is a need to exploit such data; hence, analyses are evolving and are aiming at finding the complex relations in the data. There are many techniques to approach analysis, but some techniques are in much more “fashion” and people are wanting to use them. This is the case of “machine learning” techniques, which are sometimes referred to as if the term “machine learning” was ONE specific technique, and this is observed repeatedly in grants and proposals, where phrases like “we will use machine learning to…” are used, when in fact such a term is an umbrella term for many potential techniques. Given this, it is of importance that researchers get at least a taste of what “machine (statistical) learning” is and what it is not, and what is gained/lost in the use of some of these techniques, plus knowing that it is not a magical “one click” solution. Therefore, the aim of this talk is to give an “in a nutshell” introduction to some of the most commonly used/mentioned techniques in machine learning (ML), while also making the connection to methods in general and statistical techniques. The importance of the trade-offs of variability and bias, while presenting complex concepts in a basic manner to explain what the methods do, and that ML involves more than just “clicking a button”, will be showcased using simple one-on-one variable examples. I will hone on concepts of supervised/unsupervised learning, training and testing data sets, parameters, algorithms and cross-validation.

Presenter: Alberto Nettel-Aguirre, Professor of Biostatistics, Director of the Centre for Health and Social Analytics, NIASRA, UOW

Alberto’s first exposures to ML were in his Postdoctoral years through seminars and application of techniques while mentoring graduate students. He has developed his career working collaboratively in health and medical research and has worked extensively as a biostatistician in a range of projects. His expertise and interests cover biostatistics and methods for health and social research, machine learning, social network analysis and data science, functional data analysis and big graph data. He has incorporated the use of ML techniques (e.g. CART, Nearest Neighbours, Cluster Analysis) in various health research projects in pediatric gastrointestinal disease, neurology, and applications to biomechanics in juvenile arthritis, among others.

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Contact NIASRA


Professor Marijka Batterham
Phone: 02 4221 8190

Administrative assistant

Amie Simmons
Phone: 4221 5821

Postal address

National Institute for Applied Statistics Research Australia
University of Wollongong NSW 2522

Physical address

NIASRA Administration Office
Room 263, Level 2, Building 39C (western entrance)

NIASRA is located near the P8 car park. Please contact NIASRA to arrange a parking voucher.

Parking at UOW 



02 4221 4998