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:
At our Sydney Hacky Hour sessions you can gain:
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.
Each Hacky Hour will be staffed by experts from a variety of backgrounds.
Visualisation, Python, Artemis HPC, Argus Research Desktops, astrophysics, geophysics, cloud, Matlab
Bioinformatics, genomics, transcriptomics, Metagenomics.
Alexandra (Ali) Green
Statistical methods: Statistical Modelling, Biostatistics. Programming: R, SPSS and Genstat.
Applications: Veterinary Science, Epidemiology, Life Sciences.
Machine learning, R, Python, public health, ecology
Python, web development, Linux, git, research data management
R, Python, Statistics, Machine Learning, Stata, SPSS, Linux/Ubuntu
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.
Simulation, machine learning, Bayesian statistics, physics, chemistry, R, Matlab
Data Science & machine learning, R, Linux/Ubuntu, Bioinformatics, Clinical Genomics
Bioinformatics, genomics, transcriptomics, high performance computing, genetics, R, python.
Statistical methods: Statistical Modelling, Biostatistics, Study design. Programming: R and SAS. Applications: Medicine and Health, Veterinary Science, Epidemiology, Life Sciences.
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.
Statistics and Data Science, Statistical Modelling, R, Python, Machine Learning, Multivariate Statistics
Darya Vanichkina (on leave)
Data Science & machine learning, R, python & HPC, Bioinformatics & genomics, HH founder
Python, Matlab, Linux, data science, machine learning, astrophysics
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