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Upcoming Events

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Past Events

If you are interested in any further information on our past events feel free to contact the CIC team.

Hacky Hour

Date: November 1, 2017

Time: 3pm - 4pm

Location: Library Makerspace, Level 5

Hacky Hour

Come along to code, or talk code. Are you having problems with a coding project of yours? Bring along your laptop and someone might be able to give you a hand. No RSVP necessary, just rock up!

Hacky Hour

Date: October 25, 2017

Time: 3pm - 4pm

Location: Library Makerspace, Level 5

Hacky Hour

Come along to code, or talk code. Are you having problems with a coding project of yours? Bring along your laptop and someone might be able to give you a hand. No RSVP necessary, just rock up!

CIC Seminar October 2017

Date: October 11, 2017

Time: 1pm to 2pm, followed by afternoon tea

Location: CLT Learning Space, Building 105, Room 107

CIC Seminar October 2017

Professor Dirk Ifenthaler presents Can Data and Analytics Support Learning and Teaching?

Abstract:
Remarkable repertoires of computer-based applications and systems have been developed for supporting learning and teaching. Currently, promising learning analytics applications are being developed which utilise data produced through these computer-based applications and systems. Such learner generated data and other relevant information may be further used to personalise and continuously adapt these learning environments. Accordingly, learning analytics emphasise insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. Learners may benefit from learning analytics through optimised learning pathways, personalised interventions, and real-time scaffolds. Further, learning analytics provide teachers detailed analysis and monitoring on the individual student level, allowing to identify particularly instable factors, like motivation or attention losses, before they occur.

Along theoretical foundations and empirical evidence from cognitive psychology as well as the learning sciences, this presentation will explore the readiness and promising opportunities of the emerging learning analytics field.

Bio:
Professor Dirk Ifenthaler’s research focuses on the intersection of cognitive psychology, educational technology, data analytics, and organisational learning – see Dirk’s website for a full list of scholarly outcomes at www.ifenthaler.info. Professor Ifenthaler who currently works at the University of Mannheim is the Editor-in-Chief of the Springer journal Technology, Knowledge and Learning (http://www.springer.com/10758).

The presentation will be followed by afternoon tea and networking opportunity.

CIC Visitor Seminar – September 2017

Date: September 27, 2017

Time: 2pm to 3pm, followed by afternoon tea

Location: CLT Learning Space, Building 105, Room 107

CIC Visitor Seminar – September 2017

Professor Rainer Schnell presents

Recent Developments in Bloom Filter-based Methods for Privacy Preserving Record Linkage

Abstract:
Combining data of the same unit using multiple databases is becoming increasingly popular in official statistics, the social sciences, medical research, criminology and other quantitative research fields. If the data entities are natural persons, linking different data sources is often restricted to encrypted personal identifiers when no unique personal linkage key (PID) is available. In this setting, techniques of the very active research field of Privacy Preserving Record Linkage (PPRL) have to be used.

Recently, Bloom Filters for PPRL applications have become increasingly popular, due to their similarity-preserving properties which enable the use of similarity threshold-based linkage techniques. Since these properties could lead to security vulnerabilities, the cryptographic properties of Bloom Filters are of special interest.

Previous research has shown that the straight application of Bloom Filters has a non-zero re-identification risk. Some recently developed techniques for attacking Bloom Filter encodings will be described shortly. We will present new results on new methods to make attacks on Bloom Filters increasingly difficult. These computationally simple algorithms modify the identifiers by different cryptographic diffusion techniques, among these are applications of Cellular Automata and Markov Chains. The presentation will demonstrate these new algorithms and show their performance concerning precision and recall in large databases.

Bio:
Rainer Schnell holds the chair for Research Methodology in the Social Sciences at the University of Duisburg-Essen, Germany. He is former Director of the Centre for Comparative Surveys at City University London and graduated with a postdoctoral degree in Research Methodology at the University of Mannheim in 1996. His research focuses on non-sampling errors, applied sampling, census operations and privacy preserving record linkage. Professor Schnell founded the German Record Linkage Centre and was founding editor of the journal “Survey Research Methods”. He is the author of books on Statistical Graphics (1994), Nonresponse (1997), Survey Methodology (2012), Research Methodology (10th ed. 2013) and of more than 30 papers on Privacy Preserving Record Linkage.

The presentation will be followed by afternoon tea and networking.

Introduction to Machine Learning

Date: September 14, 2017

Time: 09:00 - 17:00

Location: B100:Council Chamber (Level 3)

Introduction to Machine Learning
This workshop will introduce machine learning concepts through a mixture of lecture and hands-on coding. The aim is to teach a basic understanding of designing machine learning workflows for supervised and unsupervised learning approaches, classification and regression methods and model tuning. The workshop will be taught by Dr. Kevin Chai and Dr. Rebecca Lange from the Curtin Institute for Computation.
Who: The course is aimed at postgraduate students, researchers and professionals looking at taking their analytics skills to the next level.
Prerequisites: A working knowledge of Python and Jupyter notebooks is essential for this workshop. i.e. knowledge of basic data structures, operations and how to write scripts. The notebooks used throughout the workshop have been developed to be compatible with Python 2.7x and 3.x but may require some Python packages to be updated to the most recent version (e.g. scikit-learn). No prior knowledge of machine learning is expected.
Content: We will be giving a general overview on machine learning followed by worked examples of how to build machine learning pipelines.

Topics covered:

  • Data preparation
  • Exploratory data analysis
  • Classification
    • Cross validation
    • Learning curves
    • Model tuning
    • Reporting
  • Regression
  • Clustering
  • Dimensionality reduction
RVSP: You can register via Eventbrite, there is a small registration fee which will cover morning and afternoon tea.
Contact: Please email rebecca.lange@curtin.edu.au for more information.
If you have any special requirements to enable you to participate at this event please advise when you RSVP or email the Curtin Institute for Computation. We will contact you to provide assistance.
For information about disability services at Curtin, please visit disability.curtin.edu.au.

CIC Seminar September 2017

Date: September 13, 2017

Time: 1pm to 2pm

Location: CLT Learning Space, Building 105, Room 107

CIC Seminar September 2017

Professor Vishnu Pareek presents Modelling of Pyrolysis Process

Abstract:
Pyrolysis is an important thermochemical conversion process for the conversion of biomass into useful commodities. In this presentation, Professor Pareek will discuss the fundamentals of the pyrolysis process with special emphasis on the multi-scale aspects of its modelling. A number of case-studies for different scales of modelling will also be discussed.

Bio:
Vishnu Pareek is a Professor of Chemical Engineering at Curtin University. He has varied research interests encompassing both fundamental and applied nature research but his focus is in the area of computational fluid dynamics (CFD) and multiphase modelling.

He is currently also serving as the Head of School for the School of Chemical and Petroleum Engineering.

 

Software Carpentry Workshop August 2017

Start date: August 28, 2017

End date: August 30, 2017

Time: 09:00 - 17:00

Location: B300:219

Software Carpentry Workshop August 2017

Software Carpentry‘s mission is to help scientists and engineers get more research done in less time and with less pain by teaching them basic lab skills for scientific computing. This hands-on workshop will cover basic concepts and tools, including program design, version control, data management, and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. For more information on what we teach and why, please see our paper “Best Practices for Scientific Computing“.

Who: The course is aimed at graduate students and researchers. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

Content: We will be teaching introductions to the Unix shell, version control with Git as well as programming with Python and R. For the syllabus and schedule see

When: Monday 28 to Wednesday 30 August 2017, 9am to 5pm

Where: Building 300 : Room 219
RVSP: You can register via Eventbrite, there is a registration fee which will cover morning and afternoon tea.
Contact: Please email rebecca.lange@curtin.edu.au for more information.

Introduction to R

Start date: July 24, 2017

End date: July 25, 2017

Time: 9am-5pm

Location: B216:202

Introduction to R

The goal of this introductory workshop is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis.
A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.
This workshop is based on the Software Carpentry lesson “R for reproducible scientific analysis” and covers:
  1. Introduction to R and RStudio
  2. Project Management With RStudio
  3. Data Structures
  4. Exploring and sub-setting your data
  5. Writing functions
  6. Introduction to the tidyverse (dplyr, tidyr, ggplot2)
    1. Dataframe manipulation with dplyr and tidyr
    2. Creating Publication-Quality Graphics with ggplot2
  7. Producing reports

This lesson assumes you have R and RStudio installed on your computer.

R can be downloaded here.

RStudio is an environment for developing using R. It can be downloaded here. You will need the Desktop version for your computer.

CIC Seminar July 2017

Date: July 12, 2017

Time: 1pm to 2pm, followed by afternoon tea

Location: CLT Learning Space, Building 105, Room 107

CIC Seminar July 2017

Speaker: Dr. Andrea Bedini

 

Title: The Blockchain – A gentle introduction.

 

RSVP: Please register via Eventbrite by COB Monday 10 July 2017

 

Abstract:
In his presentation, Dr Bedini will offer a gentle introduction to the concepts behind the blockchain, answering the three big questions that arise with any new technology: What is it? How does it work? And why do we need it?

The blockchain is the disruptive technology at the base of Bitcoin, the world’s first decentralised cryptocurrency. It has received media attention and the public discussion has some enthusiasts claiming that the blockchain is the biggest invention since the emergence of the Internet.

The blockchain’s ground-breaking innovation lies in allowing people to safely conduct transactions electronically without the requirement of a trusted third-party. From its first application in the financial industry, this technology is now finding applications in many other industries such as real-estate, law, energy, and even music.

 

Bio:

Andrea Bedini obtained his PhD in theoretical physics from Milan University and has been a Research Fellow at the School of Mathematics and Statistics of the University of Melbourne. His research included mathematical combinatorics, statistical physics, genomics and traffic flow modelling, and was supported by the ARC Centre of Excellence for Mathematics and Statistics of Complex (MASCOS) and the ARC Centre of Excellence for Mathematical and Statistical Frontiers Systems (ACEMS). Andrea is also an experienced software developer and has published scientific software packages to complement his academic publications.

 

He is one of the maintainers of PyTables, a Python library for managing large datasets. After 5 years in research, Andrea has decided that developing tools for research might be more exciting than research itself, and someone else should do the grant-writing. He has now joined the Curtin Institute for Computation as a data scientist/computational specialist assisting the Curtin Business School.

CIC Research Morning Tea

Date: July 3, 2017

Time: 10:00 - 11:00

Location: Cisco IoE Innovation Centre, B216:204

CIC Research Morning Tea

If you are interested in connecting with researchers across the university to talk about research and potential multi-disciplinary projects please come along to the CIC research morning tea. RVSP by 30 June 2017 via email to curtinic@curtin.edu.au