Group of people using computers in a modern computer lab

Sydney Hacky Hour

For researchers who code or analyse data
Come to Sydney Hacky Hour to swap notes, get help, or learn new techniques in programming and data science.

Hacky Hour is a regular meetup where all researchers – students, staff and university affiliates – gather in a social environment to collaborate and get research support.

Experts and mentors from Sydney Informatics Hub and across the University will be available to advise and answer questions on coding, data analytics or digital tools.

Next session Hacky hour runs every third Wednesday of the month, 3-4pm.
See our training calendar for dates.  
Location Join virtually via Zoom:

What happens at Hacky Hour?

At our Sydney Hacky Hour sessions you can gain:

  • advice on how to best collect and manage your data
  • practical help on fixing a frustrating bug
  • an understanding of the basics of R
  • an understanding of spatial data on a map
  • help using tool 'X' after attending an Intersect training course
  • assistance in using version control
  • advice on how to access a large dataset from overseas quickly and easily

Even if you don't have a data problem to solve, come along to network with like-minded researchers, find a collaborator or hack away at your scripts in a friendly environment.

If you have a particular topic you'd like us to cover, please fill out our survey.

Meet the mentors

Each Hacky Hour will be staffed by experts from a variety of backgrounds.

Professional head shot of Nathaniel

Nathaniel Butterworth
Visualisation, Python, Artemis HPC, Argus Research Desktops, astrophysics, geophysics, cloud, Matlab

Nandan Deshpande
Bioinformatics, genomics, transcriptomics, Metagenomics.

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Alexandra (Ali) Green
Statistical methods: Statistical Modelling, Biostatistics. Programming: R, SPSS and Genstat.
Applications: Veterinary Science, Epidemiology, Life Sciences.

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Henry Lydecker
Machine learning, R, Python, public health, ecology

Mike Lynch
Python, web development, Linux, git, research data management

Professional head shot of Gordon

Marius Mather
R, Python, Statistics, Machine Learning, Stata, SPSS, Linux/Ubuntu

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Jim Matthews
Statistical methods: Experimental Design, Power Analysis, Linear Models, Meta-analysis and Statistical Inference. Programming: Excel, SPSS, Prism, R and SAS. Applications: Medicine and Health, Engineering and Materials Science.

Professional head shot of Gordon

Gordon McDonald
Simulation, machine learning, Bayesian statistics, physics, chemistry, R, Matlab

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Giorgia Mori
Data Science & machine learning, R, Linux/Ubuntu, Bioinformatics, Clinical Genomics

Georgina Samaha
Bioinformatics, genomics, transcriptomics, high performance computing, genetics, R, python.

Headshot of Kathrin Schemann

Kathrin Schemann
Statistical methods: Statistical Modelling, Biostatistics, Study design. Programming: R and SAS. Applications: Medicine and Health, Veterinary Science, Epidemiology, Life Sciences.

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Alex Shaw
Statistical methods: Experimental Design, Power Analysis, Linear Models, PCA, Clinical Diagnostic Accuracy + Agreement, Meta-analysis and Statistical Inference. Programming: R tidyverse. Applications: Medicine and Health, Molecular Biology and Genetics.

Headshot of Stanislaus Stadlmann

Stanislaus Stadlmann
Statistics and Data Science, Statistical Modelling, R, Python, Machine Learning, Multivariate Statistics

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Darya Vanichkina (on leave)
Data Science & machine learning, R, python & HPC, Bioinformatics & genomics, HH founder   

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Tim White
Python, Matlab, Linux, data science, machine learning, astrophysics

Professional head shot of Gordon

Cali Willet
Bioinformatics (Genomics, SNP and Indel Variant Detection, Structural Variation, Transcriptomics, RNA sequencing, GWAS, Metagenomics), High Performance Computing, Artemis HPC, NCI Gadi, Linux/Ubuntu, Perl, Scaling and Parallelization of Workflows