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CIC Seminar March 2018

Date: March 14, 2018

Location: tbc

CIC Seminar March 2018

Recent Splitting Schemes for the Incompressible Navier-Stokes Equations

Professor Peter Minev

Abstract:
Two different approaches to the time discretization of the incompressible Navier-Stokes equation will be discussed in this talk. The first approach relies on a particular perturbation of the continuity equation and results in a technique for incompressible flow that requires the solution of one dimensional problems only. These problems can be solved with a tridiagonal direct solver on a massive parallel cluster with a Schur complement technique. The accuracy of this class of schemes is fully comparable to the accuracy of the classical projection methods for incompressible flow.
In the second approach the artificial compressibility method for approximation of the incompressible Navier-Stokes equations is generalized. It allows for the construction of schemes of any order in time that require the solution of a fixed number of vectorial parabolic problems, depending only on the desired order of the scheme. This approach has several advantages in comparison to the traditional projection schemes widely used for the unsteady Navier-Stokes equations. The accuracy and stability of the resulting schemes will be demonstrated on examples with manufactured solutions.

Bio:
Peter Minev received his PhD degree in applied mathematics from the University of Sofia in 1991. Since 2004 he has been a full professor in applied mathematics at the University of Alberta, Canada.
He has authored or co-authored more than seventy papers in refereed journals and conference proceedings and is the advisor of twenty six graduate students and postdoctoral fellows. His general areas of interest include numerical analysis of PDEs, computational fluid dynamics and MHD, fluid mechanics and multiscale methods. Peter is a member of the Advisory Board of International Journal for Numerical Methods in Fluids, and a member of the Editorial Board of International Journal for Numerical Analysis and Modelling.

When: 14 March 2018
Where: to be confirmed
RSVP: Registration will be via Eventbrite once we have confirmed the venue.

CIC Seminar November 2017

Date: November 22, 2017

Time: 10am to 11am, followed by morning tea

Location: CLT Learning Space, Building 105, Room 107

Professor Maciej Paszynski presents

Fast and Smooth simulations of time-dependent problems

 

Abstract:
We present fast and smooth simulations of time dependent-problems, in one, two and three dimensions. We discretize the time axis, by introducing several time steps. Theus the time-dependent problem is transformed into a sequence of the stationary problems to be solved at particular time steps.

What makes the simulations smooth is the fact that we use higher order B-splines basis functions for spatial discretization at every time step. We approximate the solution at every time step with a linear combination of tensor product B-spline basis functions that span over the regular computational mesh. What makes the simulations fast, is the fact that we have the so-called alternating directions algorithm which solves these problems in every time step with the highest possible linear computational cost.

Our simulations can be performed on a multi-core laptop, and they will run for around 30 minutes. In our simulations, we can use both so-called explicit and implicit schemes. This means that we can increase the accuracy of a simulation by increasing arbitrarily the number of B-spline basis functions used.

We show how to apply our method for time-dependent simulations of several physical phenomena, including the heat transfer, non-linear flow in heterogeneous media, elastic wave propagation problem, tumor growth simulations, and the simulation of the pollutant from a chimney. We present movies generated from the performed simulations.

Bio:
Maciej Paszynski is a Full Professor of Computer Science at Department of Computer Science at AGH University, Krakow, Poland. He did his PhD in Mathematics with Applications to Computer Science from Jagiellonian University, Krakow, Poland from 2009 to 2013. He is a former postdoc of Prof. Leszek Demkowicz from the Institute for Computational and Engineering Sciences (ICES), The University of Texas at Austin (UT).

Maciej collaborates with Prof Victor Calo from Curtin University, Prof Leszek from ICES, UT, Prof David Pardo from Basque Center for Applied Mathematics (BCAM), Bilbao, Spain, Prof Keshav Pingali from ICES, UT, Prof Ignacio Muga from The Catholic University of Valparaiso, Chile, Pro. Frederick Valentine from LNCC, Petropolis, Brasil and Prof Rafael Montenegro-Armass from The University of Las Palmas de Gran Canaria.

His research interest includes fast solvers for mesh-based computations. He co-authored over 40 papers in indexed journals and gave over 100 presentations.

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.

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.

 

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 Seminar June 2017

Date: June 14, 2017

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

Location: CLT Learning Space, Building 105, Room 107

Title:

Weapons of Maths Construction: how a combination of maths theory and algorithms conquers inequalities and constructs optimal solutions.

Speaker:

Prof. Song Wang

RSVP: Please register via Eventbrite at this link by COB Monday 12 June 2017

 

Abstract:

Prof. Wang will share his experience as a numerical analyst by showcasing some of the computationally hard problems in semiconductor device modelling, optimal feedback control and financial engineering. Using straightforward and conventional methods many of these problems fail to yield accurate numerical solutions with reasonable computational costs. Thus dedicated numerical models have to be designed for them, requiring the marriage of mathematical theory and computational techniques.
Prof. Wang will give a brief introduction to some of these custom methods such as penalty methods for differential variational inequalities and mesh-base/mesh-less methods for the discretisation of partial differential equations. Both theoretical and numerical results will be discussed.

Bio:

Professor Song Wang received a B.Sc. from Wuhan University of Hydraulic and Electric Engineering, China, in 1982 and a Ph.D. in Numerical Analysis from Trinity College Dublin, Ireland, in 1989. He worked with a Dublin-based hi-tech company -Tritech Ltd., The University of New South Wales, Curtin University of Technology and The University of Western Australia, before returning to Curtin University in July 2014 as Professor and Head of Department of Mathematics & Statistics. His research interests include numerical solution of PDEs, numerical optimization and optimal control, optimum design and computational finance. He has published many research papers in these areas and his early work on numerical solution of semiconductor device equations of the drift-diffusion model has been featured in a review paper and a book chapter published respectively in a top journal Reports on Progress in Physics and a volume of the prestigious book series Handbook of Numerical Analysis. He is currently on the editorial boards of several international journals.

 

CIC Seminar May 2017 II

Date: May 18, 2017

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

Location: HIVE, Curtin University

CIC Seminar May 2017 II

Activities in Considerate Systems

Prof. Ted Selker

When: Thursday 18 May 2017, 1pm to 2pm, followed by afternoon tea.

Where: HIVE, entrance via John Curtin gallery

RSVP: https://www.eventbrite.com.au/e/cic-seminar-series-ted-selker-tickets-34497319411

Abstract:
Information systems are being called upon not only to help keep us organized and productive, but also to help in the fabric of the way we live. We are starting to see them as solving social problems as they might begin reducing disruption; they help people enjoy others or even increase self-awareness. This talk will introduce notions of how we can introduce social awareness in our design practices and artefacts.

The talk will frame the Considerate System stance of social feedback to a user. We will describe results from a variety of Considerate Research projects, with examples including systems supporting people in audio conference call communication, TV interactions, saving energy in the Sustainability Base Leeds Platinum building and Considerate Mobile phone reactions.

In working towards considerate systems, we are building CAMEO and other technology into a cyber-physical meeting support system. This ambient social feedback system includes social responses that take into account environmental sensing, interactive TV, and physical rewards. We conclude that all interactions with people in the physical world require an appreciation that they are in a social environment and engagement.

 

About the speaker:
Ted spent 5 years as director of Considerate Systems research at Carnegie Mellon University Silicon Valley. He was also responsible for developing the campus’s research mission, teaching HCI, Android product design, and research in voting with disabilities.

He is well known as a creator and tester of new scenarios for working with computing systems. His design practice includes speaking engagements, innovation workshops consulting. He is CTO of Foldimate for which he made a shirt-folding robot this year.

Ted spent ten years as an associate Professor at the MIT Media Laboratory where he created the Context Aware Computing group, co-directed the Caltech/MIT Voting Technology Project, and directed the CIDI Kitchen of the future/ product design of the future project. His work is noted for creating demonstrations of a more considerate world in which intentions are recognized and respected in complex domains.
His successes at targeted product creation and enhancement lead to his role of IBM Fellow and director of User Systems Ergonomics Research at IBM. He has also served as a consulting professor at Stanford University, taught at Hampshire, University of Massachusetts at Amherst and Brown Universities and worked at Xerox PARC and Atari Research Labs.

CIC Seminar May 2017

Date: May 17, 2017

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

Location: CLT Learning Space, Building 105, Room 107

CIC Seminar May 2017

Speaker: Dr Andrew Squelch

Title: What are our digital images trying to tell us? The role of Computational Image Analysis

RSVP: Please register via Eventbrite at this link by COB Monday 15 May 2017 at

Abstract:

A growing number of social, medical, science and engineering research activities rely on the qualitative and/or quantitative analysis of images as the basis of their investigations. The range of the discipline areas basing their findings on image data sets is growing thanks to advancements in imaging techniques and technology. These endeavours give rise to greater diversity of images and subject matter, and increased size (or resolution) and volume of image data sets to be analysed. These factors mean that manual analysis of the images is becoming less and less viable and, instead, researchers are becoming more reliant on automatic or computer-assisted analysis techniques. Hence, the increasing relevance and value of Computational Image Analysis (CIA) to extract, quantify and summarise meaningful information about features in the research image data. The CIA approach encompasses a range of different digital analysis techniques and borrows aspects from of the fields of image processing and computer vision.

This talk is intended for audiences that are interested in the qualitative characterisation of 2D or 3D digital images and wish to obtain assistance or know how they can apply the approach in their own projects. The first part of the talk is by way of a general introduction to the CIA approach, examining a few examples from applicable discipline areas and appropriate computer software. The second part highlights how CIA is being applied in some research projects conducted in the Faculty of Science and Engineering.

 

Bio:

Dr Andrew Squelch is a Senior Lecturer in the Department of Exploration Geophysics and Senior Visualisation Specialist with the Pawsey Supercomputing Centre. Andrew has over 14 years of advanced visualisation and virtual environment research experience in fields of geoscience, medical and mining. Andrew is also a member of the CIC Visualisation Theme.

 

More information:

For more details about the CIC Seminar Series, please contact: Professor David Gibson

Further information or queries: Please email Linda Lilly

Additional November CIC seminar

Date: November 30, 2016

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

Location: CLT Learning Space, Building 105, Room 107

Additional November CIC seminar

 

 

 

 

 

 

Speaker: Prof Laszlo Matyas

Title: Big Data and Econometrics - Event Count Estimation

RSVP: Please email Linda Lilly by COB Monday 28 November, 2016

About the speaker:

Professor Laszlo Matyas is a university professor at the Central European University and a former Provost, and Head of the Department of Economics at the same university. He previously held academic positons at Universite de Paris XII and Monash University, and also worked as Assistant deputy-state secretary at the Ministry of Economy in Hungary. He was the founding director of the Institute for Economic Analysis in Hungary. He also served as the Managing Director of Nortel Networks Financial Services Ltd and the Managing Director of Moore International Hungary. He has published over 50 journal papers and 10 books. His best known paper on gravity models has been cited over 800 times. His main research interests include Panel Data Econometrics, and Modelling International Trade.

Abstract: 

In classical econometrics and statistics efficient estimation has had a central role. It has been quite important to squeeze out every possible bit of information from the data. To be able to do so one had to rely one many assumptions and exact “metric”-type measures. The cost has been estimation methods which frequently are not robust against the underlying assumptions, outliers, etc. In times of big data, when almost unbounded information is available, efficiency becomes much less relevant, instead robustness and flexibility become the most desirable properties for “Big Data” estimation methods. This paper proposes a conceptually simple new technique, the so-called Event Count Estimator (hereafter ECE), which turns a blind eye towards optimality and efficiency, but is robust against several assumptions and data related problems. While optimal estimation methods (like Least Squares, Maximum Likelihood, etc.) are more efficient than the ECE when all their assumptions are satisfied, the gained (slight) precision is quickly offset by the biases optimal techniques suffer from when any (or some) of their assumptions are violated.