Sydney Hacky Hour
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: https://uni-sydney.zoom.us/j/89080313908 |
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.
Nathaniel Butterworth
Visualisation, Python, Artemis HPC, Argus Research Desktops, astrophysics, geophysics, cloud, Matlab
Nandan Deshpande
Bioinformatics, genomics, transcriptomics, Metagenomics.
Alexandra (Ali) Green
Statistical methods: Statistical Modelling, Biostatistics. Programming: R, SPSS and Genstat.
Applications: Veterinary Science, Epidemiology, Life Sciences.
Henry Lydecker
Machine learning, R, Python, public health, ecology
Mike Lynch
Python, web development, Linux, git, research data management
Marius Mather
R, Python, Statistics, Machine Learning, Stata, SPSS, Linux/Ubuntu
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.
Gordon McDonald
Simulation, machine learning, Bayesian statistics, physics, chemistry, R, Matlab
Giorgia Mori
Data Science & machine learning, R, Linux/Ubuntu, Bioinformatics, Clinical Genomics
Georgina Samaha
Bioinformatics, genomics, transcriptomics, high performance computing, genetics, R, python.
Kathrin Schemann
Statistical methods: Statistical Modelling, Biostatistics, Study design. Programming: R and SAS. Applications: Medicine and Health, Veterinary Science, Epidemiology, Life Sciences.
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.
Stanislaus Stadlmann
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
Tim White
Python, Matlab, Linux, data science, machine learning, astrophysics
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
