Sydney Informatics Hub are hosting their own Hacky Hour!
Hacky Hour is a regular meet-up where researchers can congregate to collaborate, and get research help in a social environment. Experts will be on hand to advise on problems related to coding, data analytics, or digital tools, with knowledgeable folk from SIH and other departments who can help you with your questions.
Come to a session!
How do you get involved? Just show up. If you have a laptop or tablet, bring it along so you can show what you’re working on. We have bioinformaticians, computer scientists, data scientists, data strategists, research computing and digital tool specialists, and other mentors on call to give you a hand with your problems. Check who’s rostered this session.
If you have a specific question you’d like to discuss, let us know before the session so we can find an appropriate mentor.
Hacky Hour moves around campus and visits our satellite campuses too. Next session details:
Time and Date:
3-4pm, Thursday 11th April 2019
And a special guest talk straight after Hacky Hour
Sean McMahon presents his PhD in Robotic Vision 4-5pm
Sean is a PhD candidate from the Australian Centre for Robotic Vision, he has helped run and organise numerous deep learning and machine learning courses, ranging from beginner to advanced.
He will be presenting his PhD in robotic vision about training a deep neural network to detect hazards on construction sites. Sean will share his experiences as a senior mentor at Queensland AI, where he ran multiple courses on deep learning. One of the interesting things about detecting hazards is that they are not defined by a single type of object, for example, a ladder on the ground is trippable, but not when it is leaning against a wall. Here, the ladder’s affordance, or its action possibilities, change depending on how it is placed in the environment.
Over the course his PhD, Sean had to collect and label his own datasets before training the Convolutional Neural Networks (CNN), encountering many difficulties and gaining intuitions into how to train a CNN from custom data.
ThinkSpace (Upstairs from the SciTech Library)
Level 2 - Jane Foss Russell Building G02
Get in touch with us at email@example.com if you would like to host a Hacky Hour at your research group!
People come to Hacky Hour for many reasons:
- Get advice on how to best collect and manage your data
- Have someone to show you the basics in R.
- Learn the best way to show some spatial data on a map.
- You recently attended an Intersect training course and want more help using tool ‘X’.
- Your honours student is pestering you to use version control and need help getting started.
- You need to get a huge dataset from a colleague overseas as quickly and easily as possible.
- You’d like a friendly environment to hack away at your data-crunching scripts, and maybe pick up a collaborator.
Even if you don’t have any problems you want solved, come along to:
- Network and chat with other like-minded researchers.
- Help solve someone else’s problem.
- Wrangle your data in the presence of others like-minded.
- Run your own special-interest data or analytical groups nearby and reach a wider audience.
- Help a colleague overcome their fears of using Git.
- Show off a really useful tool you just discovered.
If you’re interested in training in anything particular please let us know by filling out this survey.
Meet the mentors
Each Hacky Hour will be staffed by a variety of mentors and experts in different areas. Check below to see who’ll be around at the next session:
Nicholas Ho – Data Science
Machine learning · Transcriptomics · R · Bioinformatics · Health
Darya Vanichkina – Data Science
Bioinformatics · Genomics & Transcriptomics · Machine learning · Health · Python · R
Nathaniel Butterworth – Research Computing
Visualisation · Python · Artemis HPC · Argus Research Desktops · Astrophysics · Geophysics
Emily Neo – Econometrics
Time series · Financial/risk analysis · R · SQL · Scala · Spark
Christopher Howden – Statistician
Statistical Methods · Statistical Design
Madhura Killedar – Data Science: Statistics
Bayesian modeling · Simulation · Machine learning· R · Python · Astrophysics · Cosmology
Kayla Maloney – Visualisation
Data visualisation & communication · Design · Geoscience
Olya Ryjenko – Data consulting
Digital research tools · eNotebooks · Data capture and surveys · Speech Pathology · eHealth
|Upcoming sessions:||ThinkSpace (11 April), Physics (May), and beyond…|
Rosemarie Sadsad – Bioinformatics
Phylogenetics · long/short read DNA sequence analysis · Metagenomics · Pipelines · Modelling & Simulation · Bioinformatics & Genomics · Infectious Diseases · Health Systems
Tracy Chew – Bioinformatics
DNA/RNA sequence analysis · Association analysis · Animal genomics · Veterinary science
Creative coding · Data visualisation · Design thinking · Psychology · Educational Design · UX · Web development
David Kohn – Data Science: Bayesian modeling
Bayesian modeling · Python · R · Matlab · Stata · SPSS · SQL · Machine learning · Web
Gordon McDonald – Data Science
Simulation · Machine learning · Bayesian statistics · Physics · Chemistry · R · Matlab
Aldo Saavedra – Senior Research Scientist
Statistical Methods · Machine Learning · eHealth · Data Science · Physics
Srdjan Luzajic – Data Centre Networking
Core networking · Moving big data · Firewalls & DMZ
Usyd Hacky Hour is brought to you by..