Pawsey Internship Alumni: Celebrating Achievement!

Since 2005, more than 250 students have spent more than 2500 weeks researching and contributing to computational algorithms, data science, cloud computing, and more – through the Pawsey Summer Internship Programmes.

The story does not stop at the 2500 hours…

… These Alumni keep on giving through:

  • Activities “close to home”, e.g., participating as Pawsey Intern Mentors and Intern poster judges, and
  • Activities in the wider community, through their contributions to science, industry, government, education and more.

Pawsey Intern Alumni are recognised and celebrated here. Click on a profile below to view short summaries, or search using the filters.

If you’re a Pawsey Alumni and you would like to have your profile included, contact training@pawsey.org.au. Let’s celebrate you!

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Aashima Jaiswal
(2022/2023)
Alumn Year:   2022/2023
Cyber-bullying Detection on Social Media: A Data-driven and Deep learning-based Approach

The information flow in social networks is extensive, containing thousands of posts and reactions from people. This information is subjective and based on people’s personal experiences or opinions. One of its potential drawbacks is a significant amount of malicious users spreading doctored and fake information. Such conduct often leads to cyberbullying against individuals. Cyberbullying appears in various forms, e.g., posting offensive images or videos or sharing information without the owner’s permission. However, most commonly, it is in a textual format. Such heterogeneous data poses multiple fundamental processing challenges. Aashima’s project involves developing a hybrid model to detect these incidents.

Vocation:       Student
Institution:     ANU
Supervisors:   Dr. Tooba Aamir & Dr. Mahathir Almashor
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Abraham Rojas Zuniga
(2022/2023)
Alumn Year:   2022/2023
Bayesian Network Modelling for Predicting Stress Corrosion Cracking of Duplex Stainless Steel Alloys in Downhole Environments

Duplex stainless steel (DSS) alloys are increasingly utilised in oil and gas applications, given their high corrosion resistance. However, the susceptibility of DSS to environmentally assisted degradation (i.e., stress corrosion cracking, SCC) in production environments is not well understood, such that its operating limits in standards are perceived as overly conservative.

In Abraham’s project, a model based on Bayesian Networks (BN) is being developed to provide an explanatory framework for SCC of DSS in production environments. Nonetheless, it is required to conduct comparative studies between the results obtained by BNs and other machine learning (ML) approaches (e.g., XGBoost, Neural Networks), so as to validate the accuracy of preliminary results.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/xBwJHmRQWLg
Check out Abraham's video on his internship.
Supervisors:   Prof. Mariano Iannuzzi & Dr. Sam Bakhtiari
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Aidan Smith
(2022/2023)
Alumn Year:   2022/2023
Quantum computing for gravitational wave detection

First predicted in 1916 by Albert Einstein, the detection of gravitational waves a hundred years later ushered in a new era of gravitational-wave astronomy. The detection of gravitational waves is now a regular occurrence, and astronomers are busy using this valuable data to learn about the properties of the extreme matter in neutron stars, probe the limits of general relativity and otherwise illuminate the cosmological mysteries of the universe.

Aidan’s project aims to explore the potential for near-term quantum algorithms to assist with gravitational wave-matched filtering. It will investigate the use of several available quantum methods, including the well-known Grover’s search algorithm and the QAOA/QWOA quantum optimisation algorithms.

Vocation:       Student
Institution:     UWA
Supervisors:   Prof. Jingbo Wang & Prof. Linqing Wen
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Akbar Fadiansyah
(2022/2023)
Alumn Year:   2022/2023
Trust in Smart Devices: Deep Learning Powered Privacy Risk Analysis of Smart Space Data
Vocation:       Student
Institution:     Monash University
Supervisors:   Dr. Chehara Pathmabandu & Dr. Mahathir Almashor
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Aonghas Bradley-Moore
(2022/2023)
Alumn Year:   2022/2023
The science of cryogenics: molecular dynamics simulation of the action of cryosolvents on cell membranes

Cell membranes are the primary site of damage during cryopreservation in liquid nitrogen due to the formation of ice both inside and outside of cells. Cryosolvents are used to promote the formation of a glassy state of water and prevent the formation of damaging ice, but their presence can also be damaging to the integrity of cell membranes.

Aonghas’ project will will use molecular dynamics (MD) simulations to study the mechanism of action of cryosolvents with model cell membranes of complex lipid composition at different levels of desiccation to mimic the process of dehydration that occurs during cryopreservation, with two specific aims: (1) describe in atomistic detail the interactions of cryosolvents with cell membranes and the changes in membrane properties, and (2) conduct 3D stereoscopic visualization of these molecular events. Molecular simulation and 3D visualization will enable the elucidation of what is a complex, challenging and yet fascinating problem in cryogenics.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/CMUPRPkjmuA
Check out Anoghas' video on his internship.
Supervisors:   Prof. Ricardo Mancera & Dr Sonny Pham
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Brittany Robertson
(2022/2023)
Alumn Year:   2022/2023
Investigating sources of intraspecies and interspecies variation in the stomatal development gene network across the grass family to direct future improvement of water-use efficiency in crops.

Modification of stomatal traits improves plant water-use efficiency (WUE). Stomata have thus become a target for improving the drought tolerance of Australian crop varieties.

Brittany’s project aims to identify novel polymorphisms in genes involved in stomatal development as future targets to enhance crop WUE. As part of project outcomes, the student will determine the extent of divergence between stomatal development genes across the grass family and identify polymorphisms in stomatal development genes in barley, wheat, and oat that may have resulted from breeding or natural selection for improved WUE.

Vocation:       Student
Institution:     Murdoch University
https://youtu.be/grdVA0wecTI
Check out Brittany's video on her internship.
Supervisor:    Prof. Chengdao Li
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Carl Alvares
(2022/2023)
Alumn Year:   2022/2023
An automated Bushfire Detection System Using Space-Air-Ground Integrated Network (SAGIN).

Most bushfires in Australia usually occur in remote areas where network infrastructure is unavailable to support ICT services. Adopting Space-Air-Ground Integrated Network (SAGIN) can allow ICT services in an infrastructure-less environment like national parks in Australia to detect bushfires, initiate responses and alert other fire-prone sites. Carl’s project aims to model such a system by interfacing lightweight solar-powered sensing devices residing on the ground with UAVs providing proximate computing facilities and on-demand networking support to reach out to Cloud and Satellites. The project investigating team will also develop the required software solutions to facilitate the proposed interfacing in real-time with an optimized packet loss rate. The feasibility and performance study of different communication standards, including mobile ad-hoc networking and opportunistic 5G multicasting within UAVs, will be conducted to realize the project

Vocation:       Student
Institution:     UWA
https://youtu.be/MReL37k2fUU
Check out Carl's video on his internship.
Supervisors:   Dr. Mahbuba Afrin & Dr. Md Redowan Mahmud
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Daniil Golikov
(2022/2023)
Alumn Year:   2022/2023
Enable DALiuGE to use Acacia

The DAliuGE workflow development and execution system (https://daliuge.readthedocs.io) has been developed to support the Square Kilometre Array data processing system on an extreme scale. DALiuGE has been used to run very large workflows on the biggest supercomputers in the world, including Tianhe-2 (China), Summit (US) and, of course, also the various Pawsey systems. Daniil’s project is about enabling DALiuGE to use the Acacia object store directly through a dedicated so-called data component, which can then be used in complex scientific workflows to persist data.

Vocation:       Student
Institution:     University of Canberra
https://youtu.be/ztfAe99fCII
Check out Daniil's video on his internship.
Supervisors:   Prof. Andreas Wicenec & Mr. Nicholas Pritchard
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Deepak Kanneganti
(2022/2023)
Alumn Year:   2022/2023
Developing Resilient Swarm Intelligence for Autonomous Systems

The rising cost of fossil fuels and the general public’s increased awareness of adverse climate changes resulting from burning fossil fuels have driven up interest in renewable energy sources. The Green Electric Energy Park (GEEP) at Curtin University features state-of-the-art renewable energy-based electric power generation technology, including solar photovoltaic arrays, wind turbines, micro-hydro turbines, and fuel cell stacks. All feeds from renewable energy sources and network operating systems have created big datasets of 10 years of real-time data. We aim to effectively analyse the data using cutting-edge machine learning techniques and big data analytics to extract meaningful information. Deepak’s project will focus on advanced and accurate forecasting of renewable energy output using multivariate embedded Deep neural considering various factors such as weather, device conditions, the multivariate association between wind and solar generation, and the effects of clouds.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/qHToV29ExpI
Check out Deepak's video on his internship.
Supervisors:   Dr. Sajib Mistry & Prof. Aneesh Krishna
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Dina Kazemi Beidokhti
(2022/2023)
Alumn Year:   2022/2023
Multi-Modal Detection of Liver Cancer

Dina’s project aims at exploring advances in deep learning to detect liver cancer in patients and predict their survival using a combination of hematoxylin and eosin (H&E) images and drug/therapy response. It is expected that by combining information on the H&R images of tumors and how a patient responds to treatment, we will have a better understanding of whether a cancer is imminent. This project will develop a new deep neural network architecture based on vision transformers, which are known to be state-of-the-arts for sequence-to-sequence modelling. Here, a transformer network will be utilised to predict tumor gene expression from H&E slides. The predicted expression will be then fused with the drug/therapy responses through a fully-connected network to predict the cancer. The transformer network will be pre-trained on images and corresponding transcriptomics data of tumor samples, which will be provided by the Harry Perkins Institute.

Vocation:       Student
Institution:     University of Sydney
https://youtu.be/645L4Aqd2hQ
Check out Dina's video on her internship.
Supervisors:   Dr. Sonny Pham & Dr. Ankur Sharma
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Haadi Umer
(2022/2023)
Alumn Year:   2022/2023
Collision data for tin extreme ultraviolet lithography and fusion research

Haadi’s project aims to generate a comprehensive collision data set for electron collisions with tin ions that is relevant for modeling a hot and dense laser-driven tin plasma. Such plasma is used in extreme ultraviolet (EUV) lithography (13.5 nm wavelength) that is currently entering high-volume manufacturing to enable the continued miniaturization of semiconductor devices. Another important application of the produced dataset will be in fusion research where tin is used as a marker to monitor the erosion of wall tiles of the fusion reactor. The unique observable spectral signatures of tin ions allow to identify and locate the damage. The applicant will learn about EUV lithography and fusion research and produce a list of tin ions for quantum collision modeling. They will use quantum collision codes developed by the Curtin research group to study electron collisions with tin ions and thereby contribute to a better understanding of various processes happening in tin plasma

Vocation:       Student
Institution:     Curtin University
https://youtu.be/3Oz_36nyCro
Check out Haadi's video on his internship.
Supervisors:   Prof. Dmitry Fursa & Prof. Igor Bray
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Helen Milner
(2022/2023)
Alumn Year:   2022/2023
Automated Fraud and Money Laundering Detection techniques in Cryptocurrency Transactions

About 17% of Australians own cryptocurrency worth 8 billion AUD, although it is difficult to identify whether these digital coins are generated from a legitimate source following Australian rules and regulations. Due to the anonymous nature of blockchain, it becomes further complicated to resist fraud using the cryptocurrencies obtained through ransomware. Since no existing solution provides facilities for such risk assessment within the crypto transactions, Helen’s project aims to develop a software service for on-the-fly fraud and money laundering detection while using cryptocurrencies.

Vocation:       Student
Institution:     University of Adelaide
https://youtu.be/bMsAOJ-TCls
Check out Helen's video on her internship.
Supervisors:   Dr. Md Redowan Mahmud & Dr Sajib Mistry
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Jack Miller
(2022/2023)
Alumn Year:   2022/2023
Ultra-fast GPU molecular conformation search.

Most molecules can take more than one low-energy conformation, i.e. arrangement of their atoms. Searching for these low-energy conformations is a key problem in drug discovery. In Jack’s project, he will be developing an ultra-fast algorithm for finding low-energy molecular conformations using a combination of molecular fragmentation techniques, quantum chemistry methods and GPU programming.

Vocation:       Student
Institution:     ANU
https://youtu.be/UR0JvJoc2gk
Check out Jack's video on his internship.
Supervisor:    Dr. Guiseppe Barca
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James McGregor
(2022/2023)
Alumn Year:   2022/2023
Developing Resilient Swarm Intelligence for Autonomous Systems

The Swarm-based Unmanned System is an emerging autonomous system for various applications such as surveillance, emergency response, entertainment, and warfighting, where autonomous entities such as unmanned aerial vehicles (UAV) and unmanned underwater vehicles (UUV) are dynamically deployed with little to no human intervention. The objective of James’ project is to develop a collaborative and resilient Swarm Intelligence (SI) platform that composes and coordinates multimodal swarms for collaborative surveillance. A swarm of UAV/UUVs may act as an array of sensors that can map a large geographical area, which is impossible for a single drone or water bot. Fixed rules do not apply to the swarm systems in uncharted regions. He will analyse the efficiency of different multi-agent coordination architectures for optimal swarm activities (i.e., deform around obstacles, and reform to build the collective array of sensors) using the AIRSIM environment.

Vocation:       Student
Institution:     UWA
https://youtu.be/iMu8A8p7QuI
Check out James' video on his internship.
Supervisors:   Dr. Sajib Mistry & Prof. Aneesh Krishna
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Jiayu Li
(2022/2023)
Alumn Year:   2022/2023
How does the Ebola virus form pores in membranes to enter cells?

Viroporins are pore-forming peptides found in more than 30 viruses of relevance to human health and agriculture and are validated drug targets for the development of antiviral therapies. Because viroporins are challenging to study using traditional structural biology approaches, the development of viroporin inhibitors is hindered by the lack of high-resolution structures
Jiayu’s project will use low-resolution data from impedance spectroscopy experiments with simulations to determine the structure of viroporin in membrane environments.

Vocation:       Student
Institution:     The University of Queensland
https://youtu.be/C1XQiaKiNO0
Check out Jiayu's video on her internship.
Supervisor:    Dr. Evelyne Deplazes
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Joel Bellesini
(2022/2023)
Alumn Year:   2022/2023
DNA Jigsaw Puzzle: Streamlining Long Read Sequencing for Building Better Genomes

Marine environments contain many unique micro-organisms that have yet to isolated in the laboratory setting, yet the exploration of the marine metagenome (environmental DNA) has yielded much information into many hitherto uncultivated species. Advances in next generation sequencing and bioinformatic tools have allowed for the recovery of genomic information of individuals species directly from the environment, which has been essential in expanding our understanding of the microbial tree of life. The next stage of sequencing technology is long read sequencing, which not only improves the quality of the recovered genomes but has also been critical in uncovering biosynthetic gene clusters, a series of sequential genes which work in tandem within a metabolic process.

The goal of Joel’s project is to use the Nanopore sequencing platform for long read sequencing, along with the gold standard for DNA sequencing, Illumina, to produce sequence datasets from marine biofilms.

Vocation:       Student
Institution:     UWA
https://youtu.be/gH2eGpV-rsg
Check out Joel's video on his internship.
Supervisor:    Prof Parwinder Kaur
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Kevin Shah Mansouri
(2022/2023)
Alumn Year:   2022/2023
Genetic analysis of skin melanoma metastases

The goal of Kevin’s project is to characterize a biobank of metastatic melanoma samples from patients in WA. The samples have been fresh-frozen and transplanted to immune-compromised mice to generate so called patient-derived xenograft (PDX) mouse models. Tumors growing in the PDXs can be used to study genetics of the disease, as well as serve as source of tumor cells for drug discovery in vitro as well as in vivo. An overarching goal is to couple genetics to the pharmacological response to drugs by metastatic melanoma, to gain insight into potential biomarkers of response. To that end, we will sequence the RNA and DNA (exome) of PDX tumors and DNA of matching normal cells. The sequencing data will be 1) aligned to a reference genome, 2) used for mutation and 3) copy number analyses, as well as, 4) sample clustering analyses based on gene expression. These analyses will be suitable for a ten week project student supervised by an experienced bioinformatician in the group.

Vocation:       Student
Institution:     UWA
https://youtu.be/BR7ipmtfAuw
Check out Kevin's video on his internship.
Supervisors:   Prof. Jonas Nilsson & Dr. Joakim Karlsson+.39
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Lara Frcej
(2022/2023)
Alumn Year:   2022/2023
The science of cryogenics: molecular dynamics simulation of the action of cryosolvents on cell membranes

Cell membranes are the primary site of damage during cryopreservation in liquid nitrogen due to the formation of ice both inside and outside of cells. Cryosolvents are used to promote the formation of a glassy state of water and prevent the formation of damaging ice, but their presence can also be damaging to the integrity of cell membranes. Lara’s project will will use molecular dynamics (MD) simulations to study the mechanism of action of cryosolvents with model cell membranes of complex lipid composition at different levels of desiccation to mimic the process of dehydration that occurs during cryopreservation, with two specific aims: (1) describe in atomistic detail the interactions of cryosolvents with cell membranes and the changes in membrane properties, and (2) conduct 3D stereoscopic visualization of these molecular events. Molecular simulation and 3D visualization will enable the elucidation of what is a complex, challenging and yet fascinating problem in cryogenics.

Vocation:       Student
Institution:     UWA
https://youtu.be/wCCXXg06dro
Check out Laura's video on her internship.
Supervisors:   Prof. Ricardo Mancera & Dr Sonny Pham
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Luke Antoncich
(2022/2023)
Alumn Year:   2022/2023
Cloud-Based Quantum Computation Education with a Desktop Quantum Computer

This UWA and Pawsey Quantum Computing Center (UP-QCC) initiative will take the Triangulum three-qubit quantum computer out of the physics department and into the hands of students across disciplines. Luke’s project will involve developing an interactive browser-based workshop introducing practical quantum computing concepts in Python for the Jupyter Notebook platform featuring remote access to the Triangulum system via its SpinQKit API. By delivering the activity in-browser and leveraging cloud-based compute, these learning materials will be available cross-platform.

Vocation:       Student
Institution:     UWA
https://youtu.be/gmidSL2t70w
Check out Luke's video on his internship.
Supervisors:   Edric Matwiejew & Ann Backhaus
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Patrick Grant
(2022/2023)
Alumn Year:   2022/2023
Using membrane-active peptides to reduce the toxicity of the anti-fungal drug Amphotericin B.

Amphotericin B (AmB) is one of the most effective treatments for life-threatening invasive fungal infections, but the drug’s toxicity causes severe side effects, including chronic organ damage.

Patrick’s project studies Lactofungin (LFG), a peptide that increases the anti-fungal activity of AmB. The AMB-LFG synergy involves interaction with the cell membrane, but the details of the mechanism are not clear.

Patrick will use biomolecular simulations to understand LFG-membrane and LFG-AmB interactions to help better understand the mechanism of synergy.

Vocation:       Student
Institution:     The University of Queensland
https://youtu.be/phC3NVRUHA8
Check out Patrick's video on his internship.
Supervisor:    Dr. Evelyne Deplazes
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Sam Koshy Thomas
(2022/2023)
Alumn Year:   2022/2023
Development of a Digital Twin Testbed for Red-meat processing facility.

Sam’s project aims to develop a testbed for realising, evaluating, and fine-tuning red-meat processing operations through Digital Twins. As red-meat processing requires multiple various types of machinery, having potentially conflicting objectives, creating the exact virtual counterpart is highly complex. Hence, the proposed testbed will explicitly leverage the technological advancement in sensing and embedded computing to collect data from the physical assets within the red-meat industry and map them onto the virtual model to investigate performance bottlenecks, optimisation parameters and trade-offs.

Vocation:       Student
Institution:     University of Adelaide
https://youtu.be/x6VHUVL9Kpg
Check out Sam's video on his internship.
Supervisors:   Prof. Aneesh Krishna & Dr. Md Redowan Mahmud
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Sean Daley
(2022/2023)
Alumn Year:   2022/2023
Digital Twin-based management of Distributed Energy Resources (DERs) in Microgrids

Distributed Energy Resources (DERs) refers to various small-scale electricity generation and storage devices, including solar, battery and hydropower, that provide alternative energy sources, supplementing the traditional power grids. However, their efficient management is challenging due to the lack of understanding of how DERs work in diverse situations, locations and weather conditions. Since no existing solution mimics the behaviour of DERs, Sean’s project aims to develop a Digital Twin-based software platform for assimilating DERs’ conflicting operational dependencies and setting a knowledge base for their efficient management.

Vocation:       Student
Institution:     University of Sydney
https://youtu.be/0lTWLL-Pnhs
Check out Sean's video on his internship.
Supervisors:   Dr. Md Redowan Mahmud & Prof Aneesh Krishna
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William Hor
(2022/2023)
Alumn Year:   2022/2023
Multivariate Deep learning-based prediction of renewable energy output

The rising cost of fossil fuels and the general public’s increased awareness of adverse climate changes resulting from burning fossil fuels have driven up interest in renewable energy sources. The Green Electric Energy Park (GEEP) at Curtin University features state-of-the-art renewable energy-based electric power generation technology, including solar photovoltaic arrays, wind turbines, micro-hydro turbines, and fuel cell stacks. All feeds from renewable energy sources and network operating systems have created big datasets of 10 years of real-time data. We aim to effectively analyse the data using cutting-edge machine learning techniques and big data analytics to extract meaningful information. William’s project will focus on advanced and accurate forecasting of renewable energy output using multivariate embedded Deep neural considering various factors such as weather, device conditions, the multivariate association between wind and solar generation, and the effects of clouds.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/xpfa9f2Q6JM
Check out William's video on his internship.
Supervisors:   Dr. Sajib Mistry & Prof. Sumedha Rajakaruna
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Xinyu Tian
(2022/2023)
Alumn Year:   2022/2023
Automated Data Acquisition and Analytics for Livestock Monitoring using UAVs

In the context of Australia, due to its unique geographical distribution, it is difficult for farmers to closely monitor animal health and welfare. There is a need for innovative and autonomous solutions to keep up with demand in the agricultural industry. Xinyu’s project proposes using Unmanned Aerial Vehicles (UAVs) as an effective solution to offer an integrated wearable sensor data acquisition and analytics platform for livestock monitoring. Using computer vision algorithms, it will deploy UAVs to fly on the farm periodically and detect livestock with collars. The UAVs then fly in close proximity to the animals and establish communication with their wearable sensors. UAVs will also pull out the raw data at high speed and transmit it to the Cloud for further analysis. Data analytics methods will run in real-time at the cloud data centre to infer valuable phenomena and alert the farmers if urgent action is necessary.

Vocation:       Student
Institution:     ANU
Supervisors:   Dr. Mahbuba Afrin & Dr. Sajib Mistry
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Yaoyu Li
(2022/2023)
Alumn Year:   2022/2023
Developing a digital platform for mechanistic modelling of grinding of particles

Grinding of particles is a critical stage in mineral processing. AI models are increasingly used for online process control as the models can perform multi-variable/objective optimization. However, the current AI models are still largely driven by large amount of data with little physics involved, so their accuracy are limited to the data range/operation conditions. Yaoyu’s project is to develop a digital platform which integrates an AI model with a mechanistic model based on information obtained from Discrete Element Method (DEM) simulations of grinding processes. The platform will be applied for quick predictions of grinding process and accelerating DEM simulations.

Vocation:       Student
Institution:     UNSW
https://youtu.be/f1Ijn4_FJSA
Check out Yaoyu's video on his internship.
Supervisors:   Prof. Runyu Yang & Prof. Jie Bao
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Yutathkarn Daniel Coles
(2022/2023)
Alumn Year:   2022/2023
DNA Jigsaw Puzzle: Streamlining Long Read Sequencing for Building Better Genomes

Marine environments contain many unique micro-organisms that have yet to isolated in the laboratory setting, yet the exploration of the marine metagenome (environmental DNA) has yielded much information into many hitherto uncultivated species. Advances in next generation sequencing and bioinformatic tools have allowed for the recovery of genomic information of individuals species directly from the environment, which has been essential in expanding our understanding of the microbial tree of life. The next stage of sequencing technology is long read sequencing, which not only improves the quality of the recovered genomes but has also been critical in uncovering biosynthetic gene clusters, a series of sequential genes which work in tandem within a metabolic process.
The goal of Yutathkarn’s project is to use the Nanopore sequencing platform for long read sequencing, along with the gold standard for DNA sequencing, Illumina, to produce sequence datasets from marine biofilms. Upon conclusion of this project, the interns should be familiar with recovering Metagenome-assembled genomes (MAGs) from environmental data from short and long read sequences using bioinformatic tools (read QC, assembly, binning, etc).

Vocation:       Student
Institution:     Curtin University
https://youtu.be/rwESlwfGhrA
Check out Yutathkarn's video on his internship.
Supervisor:    Prof Parwinder Kaur
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Allen Antony
(2021/2022)
Alumn Year:   2021/2022
Automatic Detection and Assessment of Damaged Traffic Signs

The goal of this project was to train the EfficientDet neural network using a synthetic dataset of damaged traffic signs. To do this we used the Pawsey systems. We extended the model to not only detect but also assess the damage of traffic signs.

Vocation:       Student
Institution:     University of Western Australia
https://youtu.be/3YwXuyNbqWY
Check out Antony's video on his internship.
Supervisors:   Dr. Sonny Pham & Dr. Aneesh Krishna
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Allison Ng
(2021/2022)
Alumn Year:   2021/2022
Random Sparse Mixers in Quantum Optimisation Algorithms

Quantum Walk-based Optimisation Algorithms allow problems in combinatorial optimisation, such as finding the shortest route to visit a number of cities, to be solved much faster than would be possible classically. This is because in a quantum algorithm, all the possible combinations are simulated simultaneously as part of a wavefunction, and interference allows only the most optimum solution to be returned to the user.

Vocation:       Student
Institution:     University of Western Australia
https://www.youtube.com/watch?v=67RJRSMiR00
Check out Allison's video on her internship.
Supervisors:   Edric Matwiejew & Prof. Jingbo Wang
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Antonia Papasergio
(2021/2022)
Alumn Year:   2021/2022
Molecular dynamics simulation of deep Earth fluids

Antonia’s project aims to expand on the current understanding of certain geochemical properties of hydrothermal fluids. Under the supervision of Dr. Yuan Mei and Dr. Fang Huang at CSIRO (Mineral Resources), she will be modelling complex systems using the molecular dynamics approach to generate fluid property data, useful for understanding metal mobility and ore formation processes in the deep Earth.

Vocation:       Student
Institution:     University of New South Wales
https://youtu.be/d6gk1Bpzi_g
Check out Antonia's video on her internship.
Supervisors:   Dr. Yuan Mei & Dr. Fang Huang
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Caitlin Ramsay
(2021/2022)
Alumn Year:   2021/2022
Defending against phishing attacks by Human-centric AI

Phishers impersonate legitimate organisations or trusted senders by sending unsolicited emails to harvest victim credentials. Although recent advances in Artificial Intelligence boost the automatic detection of phishing attempts, it also provides hackers with opportunities to build increasingly sophisticated phishing tactics to bypass security filters. In addition, cyber criminals are exploiting human factors to take advantage of vulnerabilities, for example, during the COVID-19 pandemic phishing attempts would impersonate health organisations and include precautions of coronavirus to lure victims in clicking on links. While phishing attackers are taking advantage of human trust, curiosity, and emotions such as fears and anxiety, human skills and factors can be a powerful component in cyber defence such as cognitive function and professional judgment. The goal is to design a collaborative way that human strengths and AI harness, extend, and complement each other. This will build a sense of responsibility and trust for users and maximise resilience against phishing attacks.

Vocation:       Student
Institution:     University of New South Wales
https://youtu.be/QY-j6CNVVDI
Check out Caitlin's video on her internship.
Supervisors:   Dr. Marthie Grobler & Dr. Kristen Moore
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Calum Snowdon
(2021/2022)
Alumn Year:   2021/2022
EXESS

Calum is working with a piece of software called EXtreme-Scale Electronic Structure Software (EXESS), which computes electron behaviour in atomic systems efficiently on large-scale supercomputers. EXESS is designed and optimized for GPU-based supercomputers; Calum’s job is to port it to CPU-based systems.

Vocation:       Student
Institution:     Australian National University
https://youtu.be/weJel2JIyIo
Check out Calum's video on his internship.
Supervisor:    Dr. Giuseppe Barca
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Emily Kerrison
(2021/2022)
Alumn Year:   2021/2022
Geological simulations for mineral exploration

We already know that Northern Australia is home to several large mineral deposits, but finding new ones comes at a great cost to both bottom lines and local ecosystems. This project is about predicting the location of underground mineral deposits using geological simulations based on small, but informative, observational datasets. Emily will be trying to constrain the physical parameter space that could produce mineral deposits in the Macarthur basin using stratiagraphic models and the finite-element code MOOSE, and she will aim to optimise these geological simulations to run efficiently across the computing clusters at Pawsey.

Vocation:       Student
Institution:     University of Sydney
https://youtu.be/FopKasHNado
Check out Emily's video on her internship.
Supervisors:   Dr. Uli Kelka & Dr. Heather Sheldon
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Ewan Breakey
(2021/2022)
Alumn Year:   2021/2022
Towards Strong Privacy and Data Integrity for Internet of Medical Things

Our team aims to study the robustness of Federated Learning (FL) based Machine Learning (ML) against adversarial machine learning. In FL, data is distributed among multiple clients who collaboratively train a model based on their local summarised model. Using adversarial machine learning, a participant can corrupt the collaborative model. Our team is developing a novel defence mechanism against adversarial attacks on FL.

Vocation:       Student
Institution:     RMIT University
https://youtu.be/fT6CUfYLOYA
Check out Ewan's video on his internship.
Supervisors:   Dr. Mahathir Almashor & Prof. Ibrahim Khalil
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Ivan Kalinkin
(2021/2022)
Alumn Year:   2021/2022
GPU acceleration for antihydrogen formation calculation

Ivan’s project is in the Computational Quantum Physics field. He is working on GPU acceleration of code for an ill-conditioned atomic scattering system. This system concerns antihydrogen formation, where accurate calculations are of interest to several groups at CERN that are engaged in researching major problems in Physics, such as the Matter-Antimatter asymmetry in the Universe and behaviour of antimatter under gravity.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/PC8Lk8Fsd_M
Check out Ivan's video on his internship.
Supervisors:   Prof. Igor Bray & Prof. Alisher Kadyrov
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Jake Kelberg
(2021/2022)
Alumn Year:   2021/2022
Microstructure characterization of cementitious materials via deep learning

We are using Deep Learning to analyse micro-images of cementitious materials in order to identify relevant features so that the materials can be appropriately classified. This could result in an algorithm having the ability to judge which mixtures of cement are robust simply through viewing an image.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/2XUGM11KbO0
Check out Jake's video on his internship.
Supervisors:   Prof. Ling Li & Dr. Nadith Pathirage
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Jesse Schelfhout
(2021/2022)
Alumn Year:   2021/2022
Simulation of the Magnetic Confinement of Flowing Ions

The aim of Jesse’s project is to investigate the magnetic confinement of ions flowing through an electromagnetic field, with application to the design of fusion reactors. To approach the problem, the project will perform large-scale Monte Carlo simulations that take advantage of the parallelism enabled by the Pawsey facilities.

Vocation:       Student
Institution:     University of Western Australia
https://youtu.be/d3oiGMsWBi8
Check out Jesse's video on his internship.
Supervisors:   Dr. David Pfefferle & Mr. Martin Storey
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Joel West
(2021/2022)
Alumn Year:   2021/2022
Template BOSS: automated dynamic loading of templated classes in C++

GAMBIT, a global fitting software framework designed to automatically analyse essentially any theory beyond the standard model of particle physics, requires the ability to load C++ classes automatically and dynamically at runtime. Currently, GAMBIT has a Backend On a Stick Script (BOSS) to deal with this, however, it currently has the limitation of not being able to load templated classes. Thus, the project is to extend BOSS so that templated classes can also be dynamically loaded.

Vocation:       Student
Institution:     University of New South Wales
Supervisors:   Tomas Gonzalo & Dr. Anders Kvellestad
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John Tanner
(2021/2022)
Alumn Year:   2021/2022
Analysing Quantum Algorithms via Tensor Networks

John’s project focuses on the application of Tensor Network Notation (TNN) in the approximation and analysis of Quantum Circuits (one of the central mathematical models of Quantum Computing). During the project we will employ TNN to simulate particular Quantum Circuits and investigate how good the approximation is by comparing exact solutions with the results given by TNN.

Vocation:       Student
Institution:     University of Western Australia
https://youtu.be/M8dVCB6DR9s
Check out John's video on his internship.
Supervisor:    Prof Jingbo Wang
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Joren Regan
(2021/2022)
Alumn Year:   2021/2022
Automatic garbage localisation and classification using computer vision techniques

Joren’s project involves using deep learning on a large dataset containing images of garbage, with the intention of localising and classifying the type of trash. The aim of this project is to, at the very least, provide the bones of a system that could eventually be used to automate garbage collection worldwide via camera footage.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/0I8F8-cmkNU
Check out Joren's video on his internship.
Supervisors:   Prof. Ling Li & Dr. Qilin Li
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Joshua Macaulay
(2021/2022)
Alumn Year:   2021/2022
Microstructure characterization of cementitious materials via deep learning

Joshua’s project involves Microstructure Categorization of Cementitious Materials via Deep Learning. Using deep learning networks for microscopic image representation, analysis and generation.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/RBYOGe7gJE0
Check out Joshua's video on his internship.
Supervisors:   Prof. Ling Li & Dr. Nadith Pathirage
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Julia Ribas
(2021/2022)
Alumn Year:   2021/2022
Using membrane-active peptides to reduce the toxicity of the anti-fungal drug Amphotericin B

Julia’s project involves Molecular Dynamics simulations to predict interactions of molecules that have the potential to improve selectivity of fungal infection treatments.

Vocation:       Student
Supervisors:   Dr. Evelyne Deplazes & Prof. Alan Mark
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Mike Doan
(2021/2022)
Alumn Year:   2021/2022
Automatic garbage localisation and classification using computer vision techniques

Mike’s project involves using machine learning algorithms to collect and dispose of different variants of garbage.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/6zFOIcvzDAs
Check out Mike's video on his internship.
Supervisors:   Prof. Ling Li & Dr. Qilin Li
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Moritz Bergemann
(2021/2022)
Alumn Year:   2021/2022
Improving Semantic Scene Segmentation For Real-Time Applications

Moritz will be working under Dr Sonny Pham and A/Prof Aneesh Krishna to investigate improvements to machine learning architectures for fast semantic segmentation. This involves experimenting with a wide variety of architectures and components and performing easily comparable benchmarks to assess different designs.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/D7xB5TPzX2U
Check out Moritz's video on his internship.
Supervisors:   Dr. Aneesh Krishna & Dr. Sonny Pham
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Nicholas Antonio
(2021/2022)
Alumn Year:   2021/2022
Modelling electron capture in Be ion collisions with excited hydrogen for charge-exchange spectroscopy

Using the wave-packet convergent close-coupling approach to ion-atom collisions, Nicholas is studying bare beryllium ion scattering on excited atomic hydrogen. Specifically, he will be focussing on calculating state selective electron capture cross sections as a result of these collisions.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/ZOUjNTG8Lxk
Check out Nicholas' video on his internship.
Supervisor:    Prof. Alisher Kadyrov
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Rayna-Jade Ellem
(2021/2022)
Alumn Year:   2021/2022
The role of lipids in the maturation of the Dengue Fever virus

Rayna-Jade’s project aims to identify if blocking lipid binding sites in membrane proteins of flaviviruses can stop the membrane proteins from maturing. To identify if this is possible, simulations will be run to confirm the stability of the lipid binding sites in the mature membrane proteins.

Vocation:       Student
Institution:     University of Queensland
https://youtu.be/ykFJ-Df0v-A
Check out Rayna-Jade's video on her internship.
Supervisors:   Dr. Evelyne Deplazes & Prof. Paul Young
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Sean Oldenburger
(2021/2022)
Alumn Year:   2021/2022
Image-to-image search with deep convolutional neural networks

Sean’s project is developing a program that takes in an image and returns the top similar images from a database, called a content based image retrieval system. Through the use of deep learning techniques he will improve his model to the current state of the art method, aiming to enhance performance.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/dpxdfPcV_B0
Check out Sean's video on his internship.
Supervisors:   Dr. Qilin Li & Prof. Ling Li
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Sean Sutton
(2021/2022)
Alumn Year:   2021/2022
Image-to-image search with deep convolutional neural networks

Sean is helping out with an image retrieval project, similar to Google’s search by image. The aim is to apply different modern methodologies to better find images similar to a query image, for example, find photos of the same building but from a different angle.

Vocation:       Student
Institution:     University of Western Australia
https://youtu.be/Uio10KGZpwU
Check out Sean's video on his internship.
Supervisors:   Dr. Qilin Li & Prof. Ling Li
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Tinula Kariyawasam
(2021/2022)
Alumn Year:   2021/2022
Development of Automated Microplastic Detection using infrared reflectance spectroscopy and machine learning

The main goal of Tinula’s project is to develop a program that allows for automatic detection of microplastic with reflectance-FTIR. With Pawsey’s supercomputers, he hopes to develop and reach this aim with deep learning and different methods of unsupervised statistical analysis.

Vocation:       Student
Institution:     University of Western Australia
https://youtu.be/J9KwmoWVmko
Check out Tinula's video on his internship.
Supervisors:   Dr. Mark Hackett & Dr. Sonny Pham
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Yuval Berman
(2021/2022)
Alumn Year:   2021/2022
Temporal Tactical Formations and Recommending Substitutions in Live Soccer Analytics

The aim of our project is to use Machine Learning algorithms to investigate player-to-player interactions to direct the creation of a dynamic planning model capable of identifying and predicting the opponent team’s tactics in a live soccer match. Based on this we will aim to build a model recommending temporal tactical formations and potential substitutions based on players’ live performances.

Vocation:       Student
Institution:     University of Western Australia
https://youtu.be/_d40ZBTEUOs
Check out Yuval's video on his internship.
Supervisors:   Dr. Sajib Mistry & Prof. Aneesh Krishna
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Yvonne Mukuka
(2021/2022)
Alumn Year:   2021/2022
GPU-accelerated molecular dynamics simulations of the action of cryosolvents on model cell membranes

The project will utilise molecular dynamics (MD) simulations to investigate the mechanism of interaction of model cell membranes (phospholipid bilayers) with various sugar alcohols at different concentrations and hydration states.
Yvonne’s role is to improve on previous work by utilising a more appropriate parameter set combination.

Vocation:       Student
Institution:     Curtin University
https://youtu.be/ndXaztPa0a8
Check out Yvonne's video on her internship.
Supervisors:   Prof. Ricardo Mancera & Mr. Christopher Malajczuk
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Zelun Li
(2021/2022)
Alumn Year:   2021/2022
Template BOSS: automated dynamic loading of templated classes in C++

Zelun’s project involves updating part of a large physics simulation software GAMBIT to be more adaptable to external libraries. Currently the software works well with external libraries that have standard C++ classes. During the project the project aims to extend the code to work with template classes in C++.

Vocation:       Student
Institution:     University of New South Wales
https://youtu.be/8rN_qH9jlao
Check out Zelun's video on his internship.
Supervisors:   Tomas Gonzalo & Dr. Anders Kvellestad
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Blake Armstrong
(2020/2021)
Alumn Year:   2020/2021
GPU-accelerated molecular dynamics simulations of complex biomolecular phenomena

The project investigated the use of GPU-acceleration in molecular dynamics (MD) simulations of complex biomolecular systems. This was done using the AMBER suite of molecular simulation programs, in which a fast GPU MD simulation engine, pmend.cuda, has been developed such that the entirety of the MD calculation is performed on the GPU while the CPU core only drives the simulation.

Vocation:       Student
Institution:     Curtin University
https://www.youtube.com/watch?v=jciHno7TVp0&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=1
Check out Blake’s video on his internship.
Supervisor:    Ricardo Mancera
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Daniel Lim
(2020/2021)
Alumn Year:   2020/2021
A global phylogenetic search for bioplastic degrading genes

Polyhydroxyalkanoates (PHAs) are a family of microbially-made polyesters that are meant to quickly degrade in the environment, but this degradation is reliant on microbially-secreted PHA depolymerases, whose taxonomic and environmental distribution have not been well-defined. As a result, the impact of increased PHA production and disposal on global environments is unknown. This Intern Project searched the global databases for metagenomes to analyze the distribution of PHA depolymerase genes in microbial communities from diverse aquatic, terrestrial and waste management systems.

Vocation:       Student
Institution:     The University of Western Australia
https://www.youtube.com/watch?v=JsIYApQGc74&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=2
Check out Daniel’s video on his internship.
Supervisor:    Daniel Murphy
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David Adams
(2020/2021)
Alumn Year:   2020/2021
Latent Space Phenotyping in a High Performance Computing Environment

In 2020, a paper entitled ‘Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies’ was published in Plant Phenomics (https://spj.sciencemag.org/journals/plantphenomics/2020/5801869/). The paper outlines a novel alternative to traditional image analysis methods for phenotyping without the need for complex and bespoke image analysis pipelines. The source code has been made available (https://github.com/p2irc/lsplab). The project was developed in Python, using Tensorflow and leverages nVidia GPUs (CUDA/cuDNN). This Intern Project looked at modernising that project on supporting infrastructure (a supercomputing environment or a cloud environment).

Vocation:       Student
Institution:     The University of Western Australia
Supervisor:    George Sainsbury
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Jack Downes
(2020/2021)
Alumn Year:   2020/2021
Semantic Change Detection - Evaluation and Analysis

Semantic change detection (SCD) is an important problem for many industries. For example, rail network operators need to identify early warning signs of deteriorating conditions of supporting structure to avoid derail accidents. Where existing detection problems aim to recognise and locate known objects, SCD aims to recognise and locate characteristics of objects that deviate from what is expected. SCD is a challenging machine learning task. This Intern Project set up baselines for advanced research in SCD, by performing and analysis and then comparing several deep learning-based change detection approaches recently proposed in the literature, such as those based on Generative Adversarial Networks (GANs), Deep Convolutional Autoencoders (CAEs), and Long Short Term Memory (LSTM) models.

Vocation:       Student
Institution:     Curtin University
https://www.youtube.com/watch?v=pcXeDmgsuDA&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=3
Check out Jack’s video on his internship.
Supervisor:    Sonny Pham
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Jackson Crowley
(2020/2021)
Alumn Year:   2020/2021
Using Molecular Dynamics Simulations to understand the Membrane Interaction of Small Molecules Capable of Improving Batten’s Disease

Batten disease is a group of genetically inherited, neuro-degenerate diseases, most of which start in early childhood. Batten is always fatal, usually in the late teens or twenties and there is no treatment to reverse or halt disease progression. The overall aim of this Intern Project was to use molecular dynamics (MD) simulations, that combined with wet-lab experiments, to improve understanding of recently discovered lead molecules to treat Batten. In addition, the simulations will assist to develop technology to screen for more lead molecules.

Vocation:       Student
Institution:     University of Technology Sydney
https://www.youtube.com/watch?v=CizVLJzMBZs&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=4
Check out Jackson’s video on his internship.
Supervisor:    Evelyne Deplazes
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Jordan Makins
(2020/2021)
Alumn Year:   2020/2021
Feature extraction and Prediction models for context-aware and adaptive Analytics in Sports

Soccer is a popular sport around the world. Effective soccer analytics could improve team performance regardless of resources by maximizing the potential of existing players, analysing opponents’ game strategies, and highlighting players’ features, which are usually undervalued in predicting winning. In this Intern Project, we explored machine learning (ML) approaches to design feature extraction and prediction models for context-aware and adaptive Soccer Analytics. We explored three types of context-aware machine learning approaches: Bayesian network, Decision Tree, and Deep Neural Networks; and one advanced adaptive machine learning approach: Forest Deep Neural Network (fDNN)

Vocation:       Student
Institution:     The University of Western Australia
https://www.youtube.com/watch?v=CG1Fpq-vmHQ&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=5
Check out Jordan’s video on his internship.
Supervisor:    Sajib Mistry
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Kate Bain
(2020/2021)
Alumn Year:   2020/2021
Angular-differential electron transfer in fast proton-helium collisions

Understanding electron transfer in ion-atom collisions is essential for a variety of applications, ranging from astrophysical processes, such as solar wind and nuclear fusion, to modern cancer treatment techniques like hadron therapy. The goal of this Intern Project was to perform accurate calculations of differential cross sections for electron capture in high-energy (MeV regime) proton-helium collisions using a semiclassical wave-packet convergent close-coupling (WP-CCC) method recently developed in our group [Alladustov et al 2019 Phys. Rev. A 99 052706].

Vocation:       Student
Institution:     Curtin University
Supervisor:    Alisher Kadyrov
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Keaton Wright
(2020/2021)
Alumn Year:   2020/2021
Deep generative models for geosciences

The Intern Project explored applications of deep generative models in geoscientific model development. Applying deep learning algorithms to parameter estimation problems has received active interest from both academia and industry in recent years. Modern deep neural networks allow for fast reconstruction of various subsurface properties with a sufficient degree of accuracy. At the same time, realistic models require large datasets for training, which are not always possible to obtain from real data. Using deep generative models can significantly improve the performance. The project used the latest development in deep learning algorithms and worked with real data.

Vocation:       Student
Institution:     The University of Western Australia
https://www.youtube.com/watch?v=z6BniVBCqdk&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=6
Check out Keaton’s video on his internship.
Supervisor:    Vladimir Puzyrev
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Mitchell Gill
(2020/2021)
Alumn Year:   2020/2021
Optimising machine learning pipelines for genomic prediction

Genomic prediction has been a staple of plant and animal breeding, but ‘classic’ approaches cannot suitably model large datasets or complex additional datasets, such as time-series weather data. ‘Modern’ approaches such as the construction of Artificial Neural Networks can be highly effective for prediction of complex data. The Intern Project built ensembled Artifical Neural Networks that took already existing genomic and weather data in two streams to calculate phenotypic predictions. The input data was divided into training, testing and hold-out sets, where the neural network was built upon training and testing data and ideally could accurately predict phenotypes from the hold out set. Once built the Neural Networks were further optimised, taking advantage of the GPU backend for optimal hyper-parameter selection, to improve the phenotypic predictions in crop breeding.

Vocation:       Student
Institution:     The University of Western Australia, Australia
https://www.youtube.com/watch?v=Yy_gvQ7hEHI&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=7
Check out Mitchell’s video on his internship
Supervisor:    David Edwards
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Rebecca Green
(2020/2021)
Alumn Year:   2020/2021
Large scale simulations of electrified liquid-liquid interfaces

The transfer of ions across liquid-liquid interfaces is key to many technological applications, such has heavy metal extractions and sensing. Although, there is a phenomenological understanding of how this process occurs, there is no detailed molecular picture of the ion transfer process in the presence of electric fields. The group has recently developed a method to correctly simulate the effect of an external electric field in heterogenous systems, which was tested on the interfaces between two immiscible liquids, water and 1,2-Dichloroethane (DCE) (the most commonly used system for sensing applications). This Intern Project continued this work by studying the properties of the water/DCE interface in the presence of electrolytes on both sides of the interfaces, focusing on determining how the structure of the interface changes with the concentration of the electrolytes and on computing the transfer potential of various ions from one liquid phase to the other.

Vocation:       Student
Institution:     The Australian National University
https://www.youtube.com/watch?v=D835Z3ZtiW8&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=8
Check out Rebecca's video on her internship.
Supervisor:    Paolo Raiteri
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Reese Horton
(2020/2021)
Alumn Year:   2020/2021
Particle transport simulations using Monte Carlo method

This Intern Project focused on computational and theoretical physics based on high-performance computing, specifically implementing a new parallelization framework for the Monte Carlo simulation computer code and performing relevant modeling calculations. In the Project, Reese studied charged particle transport in a hydrogen-helium plasma, which is particularly relevant to fusion research. The present version of the code was implemented on one node with no parallelization. Monte Carlo simulation code is very computationally intensive and appropriate parallelization needed to be implemented. In addition, approaches to visualization of the obtained results were investigated and implemented.

Vocation:       Student
Institution:     Curtin University
https://www.youtube.com/watch?v=h0Y9ORjEEnk&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=9
Check out Reese's video on his internship.
Supervisor:    Dimitry Fursa
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Tavis Bennett
(2020/2021)
Alumn Year:   2020/2021
Quantum Combinatorial Optimisation

We now know a quantum computer can solve an enormously large set of linear equations, can simulate a wide range of Hamiltonians representing chemical and biological systems, can perform various linear transformations including Fourier transforms, and can efficiently evaluate inner products and distances in super high dimensional vector space, the last of which is particularly useful in machine learning. In this Intern Project, we explored potential applications in combinatorial optimization, which are known to be notoriously difficult to solve, even approximately in general. The group recently developed a promising quantum algorithm, taking advantage of intrinsic quantum correlations and quantum parallelism, to deal with combinatorial optimization problems that scale up exponentially. The Intern Project helped to validate this algorithm through large-scale high-performance simulation of an actual quantum computer.

Vocation:       Student
Institution:     The University of Western Australia
https://www.youtube.com/watch?v=4myfyya7D3s&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=10
Check out Blake's video on his internship.
Supervisor:    Jingbo Wang
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Thai Nguyen
(2020/2021)
Alumn Year:   2020/2021
Teenage Sleep Study Visualisation

Thai took the sleep dataset that is being collected globally by students and, with the support of the Pawsey Visualisation Team, developed interactive web-based visualisations to enable high school students to explore and understand the dataset. The Intern Project is being undertaken with the goal of further growing the dataset and expanding the initial portal to include STEM educational materials and “voices” of scientists and experts.

Vocation:       Student
Institution:     The University of Western Australia
https://www.youtube.com/watch?v=CRWtcDEHFxI&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=11https://www.youtube.com/watch?v=CRWtcDEHFxI&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=11
Check out Thai's video on his internship.
Supervisor:    Linda McIver
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Tushar Nagar
(2020/2021)
Alumn Year:   2020/2021
Automatic detection of Mercurian impact craters - preparation of the Bepi Columbo mission

Impact craters across the surface of planetary bodies are of great importance to understand the formation and the evolution of celestial bodies. Secondary craters result from the debris ejection from a primary impact and lead to the formation of long chains of smaller craters on the surrounding ground. The team developed a Crater Detection Algorithm trained on Mars, detecting 94 million impact craters > 25m in diameter. The team is retraining the algorithm on the Moon, and will then turn its attention to Mercury. Mercury exhibits the most unusual secondary crater population in the Solar System. The analysis of secondary craters smaller than 1 km in diameter has never been performed because they are too numerous to be counted by hand. The goal of this Intern Project is to perform analysis on Mercury using the Messenger/MDIS-NAC (1.1m/px) by creating a training dataset using this set of imagery, to retrain the current model. The resulting automatic impact crater catalog will be used by the Bepi-Columbo mission to help target areas of interest.

Vocation:       Student
Institution:     Monash University
Supervisor:    Konstantinos Servis
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Xavier Barton
(2020/2021)
Alumn Year:   2020/2021
Binning ticks: Developing pipelines for analysing tick metagenomics

In recent years, public and government concern in Australia about the potential for tick-borne diseases in people has increased considerably. Uncertainty about Australian Lyme disease-like illness requires evidence-based science to identify the microorganisms responsible and provide conclusive data about the speed of infection after tick attachment. This Intern Project identified appropriate bioinformatic pipelines to assign taxonomy to multiple sourced samples (tick, vertebrate host, microbe), which contributed to ultimately improve diagnostic tests, treatment protocols, and the control of tick-borne diseases

Vocation:       Student
Institution:     Murdoch University
https://www.youtube.com/watch?v=bVr78tV2I8Q&list=PLmu61dgAX-aa1DW3RZ2abc_MNiicfT28x&index=12
Check out Xavier’s video on his internship
Supervisor:    Charlotte Oskam
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Adam Singor
(2019/2020)
Alumn Year:   2019/2020
Photon collisions with atoms and molecules: Rayleigh and Raman scattering

Rayleigh and Raman scattering: This research project in computational and theoretical physics used Pawsey’s high-performance computing, implementing a new parallelization framework for the photon collision computer code and performed relevant modeling calculations. The problem of photon-atom scattering was addressed using a fully quantum approach based on the evaluation of the Kramers-Heisenberg-Waller (KHW) matrix elements. Appropriate parallelization was implemented to improve computational performance

Institution:     Curtin University
Supervisor:    Dimitry Fursa
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Ashling Charles
(2019/2020)
Alumn Year:   2019/2020
Mapping conservation in the human genome with single-base-pair resolution

The goal of the Intern Project was to develop a containerised workflow solution for DNA Zoo genome alignments to human genome using the LASTZ sequence alignment program. The project planned to take advantage of the HPC and Nimbus Research Cloud architecture at Pawsey’s to test the primary alignment processing stages, using the DNA Zoo genome assemblies of diverse mammal species to human. This work is foundational to doing any comparative work, and to a key desideratum: mapping conservation in the human genome with single-base-pair resolution

Vocation:       Computer Science Intern
Institution:     Water Corporation, Australia
Supervisor:    Parwinder Kaur
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Callan Wood
(2019/2020)
Alumn Year:   2019/2020
GPU Acceleration of Carbon Molecular Dynamics Simulations

The goal of the Intern Project was to port the EDIP interatomic potential developed by the Curtin Carbon Group to GPU-enabled systems. This project continued the development of HPC capability in the Curtin Carbon Group. A number of years ago the group ported the EDIP interatomic potential to LAMMPS as part of a Pawsey Internship Project. The routines proved extremely valuable, underpinning a successful ARC Discovery Project and establishing the group as international leaders in this field. By expanding our capability into GPUs, the group planned to continue to push the boundaries of what is possible with molecular dynamics simulation.

Supervisor:    Nigel Marks
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Charlie Rees
(2019/2020)
Alumn Year:   2019/2020
Optimised automatic 3D geophysical inversion for HPC infrastructure

3D geophysical inversion is a core method for resolution of the subsurface, for a wide range of applications, but in particular is used for minerals and petroleum exploration. The goal of this Intern Project was to derive new workflows to build a “one step” process to generate 3D geophysical inversion models from native-format data, as is collected in airborne surveys. This data is inherently anisotropic as it is collected along long lines, densely sampled (e.g. 10m), but with a much greater separation between lines (e.g. 400 m). Most inversion procedures require a number of pre-processing steps, which are sub-optimal (e.g., time consuming, extensive manual input, numerous assumptions).
Modern approaches are taking advantage of HPC infrastructures that permit much more comprehensive and precise models to be implemented. The ability to rapidly and rigorously build 3D models is burgeoning as “live-data” environments and on demand services become more common

Supervisor:    Alan Aitken
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Edric Matwiejew
(2019/2020)
Alumn Year:   2019/2020
Simulation of Quantum Statistical Algorithms

Edric worked on the simulation of quantum statistical algorithms. The key to this project was the calculation of extremely large matrix exponentials using algorithms parallelised by MPI. These codes simulated the quantum statistical algorithms that were proposed by the quantum research group at UWA.

Vocation:       PhD Student
Institution:     The University of Western Australia, Australia
https://youtu.be/dEpPbwKL13g
Watch Edric's video from their internship at Pawsey
Supervisor:    Prof Jingbo Wang
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Jack Mutrie
(2019/2020)
Alumn Year:   2019/2020
Exploring structure, dynamics and aggregation propensity of Apolipoprotein A-I (ApoA-I)

The ability of proteins to fold spontaneously in their native structure or functional state is essential for biological function. Failure to fold in the native shape may lead to misfolding and aggregation of proteins into insoluble aggregates, known as amyloid fibrils. These fibrous deposits have been linked to debilitating and age-related diseases, such as Alzheimer’s, Parkinson’s, type-II diabetes and others. The Intern Project studied the role of mutations on the structure, dynamics and aggregation propensity on the lipid-oriented protein: apolipoprotein A-I (apoA-I). The accumulation of this protein as amyloid fibril has been associated with atherosclerotic plaques. The work was done in collaboration with the experimental research group led by Dr Michael Griffin from the Bio21 Institute and University of Melbourne.

Supervisor:    Nevena Todorova
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Jarrod Greene
(2019/2020)
Alumn Year:   2019/2020
A deep learning approach to evaluating mechanical properties

The Intern Project aimed at calibrating a multiphysics geomechanical simulator against experimental data using a Deep Learning (DL) approach. A specificity of the simulator used was its novel constitutive model controlling the mechanical behaviour from state variables like temperature and pore pressure. Since those properties were not directly measured, the calibration could only be obtained through an inversion process. Traditional approaches to inverse problems are largely based on deterministic gradient-based methods, which are limited by non-linearity and non-uniqueness of large-scale problems in high-dimensional parameter spaces. The non-linear physical couplings involved in multiphysics problems make this process extremely challenging, even for expert users, and therefore are particularly suitable for Artificial Intelligence (AI) methods.

Supervisor:    Vladimir Puzyrev
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Joel Dunstan
(2019/2020)
Alumn Year:   2019/2020
Critical Infrastructure Monitoring with Deep Learning

This Intern Project evaluated and compared state-of-the-art semantic segmentation methods (DeconvNet, UNet, SegNet, PSPNet, FastSCNN, DeepLabV3) for critical infrastructure monitoring. Semantic segmentation is usually the first task in any scene analysis application, providing useful information about the different foreground and background objects in the scene. The project compared the methods on benchmark segmentation datasets, and then applied them to specific applications where scenes containing critical infrastructure needed to be analysed. The project recommended the most suitable methods based on the overall speed and accuracy.

Vocation:       Sales Assistance
Institution:     Jaycar Electronics
Supervisor:    Sonny Pham
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Joseph Sigar
(2019/2020)
Alumn Year:   2019/2020
Building HPC workflows to detect novel repeat expansions in next-generation sequencing data

Repeat expansions of short tandem repeats (STRs) are responsible for over twenty-five human neurological disorders, including Huntington disease, spinocerebellar ataxias and intellectual disabilities (e.g. Fragile-X). Many disorders showing anticipation go undiagnosed as we do not know all the possible repeat expansions. Next-generation sequencing (NGS) may be used to detecting novel repeat expansions but requires computationally intensive algorithms. The goal of this Intern Project was to scan for novel repeat expansions genome-wide in hundreds of NGS samples by creating analysis pipelines using a workflow manager to help analyse NGS samples for evidence of repeat expansions and by incorporating the running of several packages for repeat detection, including HipSTR, STRetch, ExpansionHunter and exSTRa.

Vocation:       Biometrician
Institution:     Statistics for the Australian Grains Industry
Supervisor:    Nicola Armstrong
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Mitchell Cavanagh
(2019/2020)
Alumn Year:   2019/2020
ew Martian impact craters detection

The aim of this Intern Project was to automate the detection of new impact craters on the surface of Mars by using a Crater Detection Algorithm. The pipeline of data treatment involved training on images containing already known new impact craters, which were then applied on all high-resolution imagery dataset currently available, with preferential focus on dust-free regions.

Vocation:       PhD Student
Institution:     International Centre for Radio Astronomy Research (ICRAR)
Supervisor:    Anthony Lagain
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Tarun Bonu
(2019/2020)
Alumn Year:   2019/2020
Characterisation and comparative analyses of immune genes in marsupials

Tarun worked on the characterisation and comparative analyses of immune genes in marsupials. The goal of the project was to develop a containerised workflow solution to map the already characterised 800 genes vital to the immune response in the human genome for the 18 marsupial genomes available now. Among these genes are the highly divergent immune genes, such as cytokines, natural killer cell receptors, and antimicrobials. The work revealed the level of complexity of the marsupial immunome as compared to the human.

Vocation:       Data Scientist
Institution:     Monash University
Supervisor:    Sonika Tyagi
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Ailin Guan
(2014/2015)
Alumn Year:   2014/2015
Image Deblurring for improved 3D reconstructions

Images deblurring is a newly arising research area, especially for improving 3D reconstructions which are commonly used in many disciplines. Images are not always captured under ideal circumstances, which will result in lower quality images. The degraded quality images can affect the quality of some 3D reconstruction features; this will eventually lead to difficulty of 3D reconstruction, missing parts and holes in the 3D models. This project is to find an effective deblurring algorithm for images that are taken underwater for 3D reconstruction process. We focus on deblurring blurred images caused by defocus and motion, as either type of these blurred images will destroy details we need in 3D reconstruction process.

https://vimeo.com/showcase/3323011/video/123269445
Click here to view Ailin's presentation
Supervisors:   Dr Andrew Woods (Curtin University) & Dr Petra Helmholz and Joshua Hollick
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Alex Bennet
(2014/2015)
Alumn Year:   2014/2015
Meshing and Visualisation of large CFD datasets

Alex participated in the Pawsey Summer Internship Program in 2014/2015. Alex participated through Curtin University, School of Public Health.

Accurate understanding and simulation of airflow through lungs could lead to advances in aerosol medicine delivery, knowledge of lung function and other benefits to public health. Computational fluid dynamics (CFD) simulations of this kind have been carried out previously using the open source software OpenFOAM, however the sensitivity to certain input variables on lung models is yet to be tested and doing so will be important for their validation. This work looks at the sensitivity of lung airflow simulations to the expansion ratio of the lung model itself, i.e. the ratio between the lung before a breath is taken and the lung at full inflation.

Vocation:       UWA
https://vimeo.com/showcase/3323011/video/123269446
Click here to view Alex's presentation
Supervisors:   Ben Mullins & Andrew King and Ryan Mead-Hunter
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Alexander Bray
(2014/2015)
Alumn Year:   2014/2015
An alternate approach to solving quantum collision equations: Solving the CCC equation without a singularity

Alexander participated in the Pawsey Summer Internship in 2014/2015. He participated through Curtin University, Theoretical Physics.

The probabilities involved in atomic collisions can be calculated through a method known as convergent close coupling (CCC). The existing technique of solving the CCC equation involves integration over a singularity. Here, we present an alternate method using an analytical solution to the Green’s function, which yields the same results as the original formulation and yet is free from singularities.

Institution:     Curtin University
https://vimeo.com/showcase/3323011/video/123269447
Click here to view Alex's presentation
Supervisors:   Igor Bray & Prof Dmitry Fursa and Associate Prof Alisher Kadyrov
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Benjamin Courtney-Barrer
(2014/2015)
Alumn Year:   2014/2015
A Clock for the Square Kilometre Array

The Square Kilometre Array (SKA) is a global next-generation radio telescope project aiming to answer fundamental questions about our universe. A key technical challenge in building the SKA is providing a stable clock reference to each antenna. We have designed and tested a system which can stably disseminate a clock signal (10MHz) over a fibre network while actively compensating fluctuations in the fibre. The system has a stability of ~10-4 at 1s for up to 5km lengths, and has recently been verified on the Australian Square Kilometre Array Pathfinder (ASKAP) telescopes.

https://vimeo.com/showcase/3323011/video/123272963
Click here to view Benjamin's presentation
Supervisors:   Dr. Sascha Schediwy (UWA) & Dr. Sascha Schediwy (UWA)
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Hannah Klinac
(2014/2015)
Alumn Year:   2014/2015
Data interaction using human-computer interaction devices on 3D displays

Hannah participated in the Pawsey Summer Internship Program in 2014/2015. She participated through the Pawsey Supercomputing Centre.

Supervisors:   Dr Andrew Squelch & Yathunanthan Sivarajah and Paul Bourke
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Jonathan Teo
(2014/2015)
Alumn Year:   2014/2015
Exploring the Plausibility of Extracting Dimensional Measurements of Fish from Single-Camera Footage

This project aims to explore the plausibility of extracting dimensional measurements of fish from single-camera underwater footage. The extracted data could lead to estimates of body mass/biomass which can be used as indicators of fish health and stress. At present this is generally done by capturing live fish or using expensive stereo-camera setups.

The ability for researchers to monitor a marine habitat less invasively using cameras would reduce the impact they have on the habitat. The ability to analyse single-camera footage could add value to the existing abundance of underwater footage. Large volumes of video could be analysed, producing new data from old resources. Various methods were explored over the course of eothis project: photogrammetry/3D reconstruction from still frames; object recognition using cascading classifiers; and image segmentation using background subtraction.

Institution:     Curtin University
https://vimeo.com/showcase/3323011/video/123272965
Click here to view Jonathan's presentation
Supervisor:    Dirk Slawinski (CSIRO Life & Environmental Sciences)
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Kirsty Butler
(2014/2015)
Alumn Year:   2014/2015
The galactic structures of radio galaxies - a first look at what GAMA can tell us about MWACs sources

This project is a first step in investigating the galactic structures of radio galaxies in the Murchison Widefield Array Commissioning Survey (MWACS) and Galaxy And Mass Assembly (GAMA) catalogues. I look at the 69 deg^2 G23 region, yielding 40 matches and a smaller catalogue of 15 sources with redshifts. A full cross-match between all the GAMA regions and the new GLEAM survey is thus predicted to have ~200 matches for which a more comprehensive study of radio host galaxies can be made.

https://vimeo.com/showcase/3323011/video/123272967
Click here to view Kirsty's presentation
Supervisors:   Professor Carole Jackson & Professor Simon Driver
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Lewis Howard
(2014/2015)
Alumn Year:   2014/2015
Convergence of Ionization Cross Sections in the CCC Method for Electron - Atom Scattering

This project explored how large a Laguerre basis is needed in the Convergent Close-Coupling (CCC) method before convergence in the estimate of the SDCS is reached for electron scattering on atomic Hydrogen in the S-Wave model. The study found that a Laguerre basis size of 16 resulted in an estimate that was converging for incident electrons of 44.4 eV, a basis size of 17 was required for 54.4 eV electrons and a basis size of 19 for 64.4 eV electrons.

https://vimeo.com/showcase/3323011/video/123272966
Click here to view Lewis's presentation
Supervisors:   A/Prof. Alisher Kadyrov & A/Prof. Alisher Kadyrov
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Liu Yi
(2014/2015)
Alumn Year:   2014/2015
High performance 3D Reconstruction

3D reconstruction using images has become more important in different disciplines such as heritage mapping and marine science. Some of these image datasets are very large and require numerous days of processing. The goal of this project is to implement a parallel solution on the iVEC supercomputers to reduce computing time allowing a time realistic processing of large image datasets.

https://vimeo.com/showcase/3323011/video/123279688
Click here to view Liu's presentation
Supervisors:   Dr. Petra Helmholz & Joshua Hollick, Dr. Andrew Woods (Curtin University)
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Mitchell Chiew
(2014/2015)
Alumn Year:   2014/2015
We’re getting closure: New solutions for pulsar searching in the SKA era

In this project we look at the performance of the bispectrum method for detecting transient sources in interferometric data. We test a relation predicted to exist between the signal to noise ratio (SNR) of an interferometric detection of a pulsar versus the channel width of visibility data in the correlator. We used data gathered from the Giant Metrewave Radio Telescope (GMRT), and formed the closure triangles from those antennas to synthesise a time-domain signal from the combined array. We test a newly derived analytical relationship between SNR and number of channels and demonstrate that it is possible to use the bispectrum method to achieve better transient detection than ever before.

https://vimeo.com/showcase/3323011/video/123279689
Click here to view Mitchell's presentation
Supervisors:    Dr Richard Dodson & Dr Ramesh Bhat (ICRAR)
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Peter Kroeger
(2014/2015)
Alumn Year:   2014/2015
How to use a basic OpenFOAM case for wave analysis

This project was undertaken to assist Dr Andrew King in the process of developing a Computational Fluid Dynamics (CFD) simulation that analyses and estimates the potential power generation from two wave energy harvesting device designs. Due to the time constraints of the internship, this project focussed on the preliminary aspects of this analysis – calculating the forces on the wave energy harvesting devices by a wave scheme that replicates the conditions found off the coast of Perth, Western Australia.

https://vimeo.com/showcase/3323011/video/123279690
Click here to view Peter's presentation
Supervisor:    Dr Andrew King (Curtin University)
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Reece Harvey
(2014/2015)
Alumn Year:   2014/2015
Radio galaxies as revealed by the GLEAM and AT20G surveys

In order to aid the process of interpreting data across a very broad frequency range, we have developed a new technique for comparing the spectral forms of various types of radio galaxy, and defined a set of classifications based on these forms, successfully linking them back to the current understanding of radio galaxy behaviour.

https://vimeo.com/showcase/3323011/video/123062447
Click here to view Reece's presentation
Supervisor:    Dr Tom Franzen and Dr John Morgan
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Sam McSweeney
(2014/2015)
Alumn Year:   2014/2015
Massively Parallel Simulations for Carbon

The environment-dependent interaction potential for carbon (CEDIP) is implemented in LAMMPS, and its parallel efficiency is benchmarked using the Pawsey Centre’s Magnus supercomputer. CEDIP performs significantly better than the original (non-LAMMPS) version for large numbers of CPUs, and the same as (or, for larger systems, slightly poorer than) other carbon potentials.

https://vimeo.com/showcase/3323011/video/123279694
Click here to view Sam's presentation
Supervisors:   Dr. Nigel Marks & Dr. Paolo Raiteri (Curtin University)
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Samuel Warnoch
(2014/2015)
Alumn Year:   2014/2015
Connect the Kinect With Dome and Cylinder 3D Game-based Environments

Developing camera-tracking applications and other interface devices using cheaply available commercial software and hardware that allow participants to have their gestures, movements, and group behaviour be fed into the virtual environment either directly or indirectly in order for presenters to present 3D virtual worlds to remotely located audiences while appearing to be inside those virtual worlds has immediate practical uses.

https://vimeo.com/showcase/3323011/video/123279691
Click here to view Samuel's presentation
Supervisor:    Erik Champion (Curtin University)
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Thuy Pham
(2014/2015)
Alumn Year:   2014/2015
Interfacial Properties of Methanol – Water and Ethanol –Water Mixtures

The interfacial properties of methanol-water and ethanol – water mixtures were studied by using molecular dynamics simulation. The surface tension, density distributions of water, methanol, ethanol and their hydrophobic and hydrophilic groups were analysed. The results show good agreements to the previous literatures. It is followed by studying the angle between the water dipole and the positive z-axis of the simulation box in terms of cosine. The presents of positive peaks at different concentrations of the alcohol confirm the existence of the second water layer. The water density from the vapour phase to the positive peak of the water dipole order was analysed in both systems. This new adoption of the second water layer has helped to quantify the amount of the water molecules, which have specific orientations at different alcohol compositions and confirm a relation to the surface tension.

https://vimeo.com/showcase/3323011/video/123279759
Click here to view Thuy's presentation
Supervisors:   Dr Chi M.Phan & Mr Cuong V. Nguyen (Department of Chemical Engineering, Curtin University)
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Dylan McCarthy
(2012/2013)
Alumn Year:   2012/2013
SkuareView Client
https://vimeo.com/showcase/3323012/video/123389909
Click here to view Dylan's presentation
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Jack Moore
(2012/2013)
Alumn Year:   2012/2013
Why must we live in a world without Giant Ants?
https://vimeo.com/showcase/3323012/video/123391594
Click here to view Jack's presentation
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Jackson Bailey
(2012/2013)
Alumn Year:   2012/2013
Positronium Formation in Positron-Hydrogen Scattering
https://vimeo.com/showcase/3323012/video/123389911
Click here to view Jackson's presentation
Supervisors:   Dr Alisher Kadyrov & Prof Igor Bray, Institute of Theoretical Physics - Curtin University
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John Snadden
(2012/2013)
Alumn Year:   2012/2013
SCIENCE! Geothermal Energy
https://vimeo.com/showcase/3323012/video/123388903
Click here to view John's presentation
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Josh Izaac
(2012/2013)
Alumn Year:   2012/2013
Micromagnetism and GPUs
https://vimeo.com/showcase/3323012/video/123388899
Click here to view Josh's presentation
Supervisors:   Dr Peter Metaxas & Dr Vincent Baltz
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Joshua Hollick
(2012/2013)
Alumn Year:   2012/2013
3D Reconstruction of the HMAS Sydney II & HSK Kormoran
https://vimeo.com/showcase/3323012/video/123388900
Click here to view Joshua's presentation
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Ken Di Vincenzo
(2012/2013)
Alumn Year:   2012/2013
Visualising the Role of Topology in the Transformation of Carbon Onions to Nanodiamonds
https://vimeo.com/showcase/3323012/video/123389905
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Ken Hing Yeong
(2012/2013)
Alumn Year:   2012/2013
Whale Pattern Matching Upgrade
https://vimeo.com/showcase/3323012/video/123391595
Click here to view Ken's presentation
Supervisor:    Mark Case
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Matt Drage
(2012/2013)
Alumn Year:   2012/2013
Improvement of the Execution Time of a Cellular Automata Model for Subsidence Prediction
https://vimeo.com/showcase/3323012/video/123391596
Click here to view Matt's presentation
Supervisor:    Dr Jose Saavedra Department of Mineral and Energy Economics
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Rebecca Tung
(2012/2013)
Alumn Year:   2012/2013
3D Hydrothermal Simulations of the Perth Basin
https://vimeo.com/showcase/3323012/video/123391869
Click here to view Rebecca's presentation
Supervisors:   Heather Sheldon & Thomas Poulet and Peter Schaubs CSIRO
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Scott Thomas
(2012/2013)
Alumn Year:   2012/2013
Galaxies and the Electromagnetic Spectrum
https://vimeo.com/showcase/3323012/video/123388902
Click here to view Scott's presentation
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Thomas Loke
(2012/2013)
Alumn Year:   2012/2013
Quantum Compilers and Jigsaw Puzzles
https://vimeo.com/showcase/3323012/video/123389908
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Andrew Cannon
(2011/2012)
Alumn Year:   2011/2012
Compression and Multi-resolution for Radio-astronomy Images
https://vimeo.com/showcase/3323013/video/123394676
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Chris Malajczuk
(2011/2012)
Alumn Year:   2011/2012
Cryoprotecting Agents
https://vimeo.com/showcase/3323013/video/123583489
Click here to view Chris's presentation
Supervisors:   Prof Ricardo Mancera & Dr Zak Hughes
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Chris Murphy
(2011/2012)
Alumn Year:   2011/2012
Quantum Discord
https://vimeo.com/showcase/3323013/video/123393861
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Grace Beven
(2011/2012)
Alumn Year:   2011/2012
Extracting 3D Models from Images of the HMAS Sydney Wreck
https://vimeo.com/showcase/3323013/video/123393860
Click here to view Grace's presentation
Supervisors:   Dr Andrew Hutchinson, Curtin University & Andrew Woods and Paul Burke
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Harrison Black
(2011/2012)
Alumn Year:   2011/2012
Input Output Server
https://vimeo.com/showcase/3323013/video/123394672
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Jianxiong Dai
(2011/2012)
Alumn Year:   2011/2012
CO2 Storage in Geological Formations
https://vimeo.com/showcase/3323013/video/123394675
Click here to view Jianxiong's presentation
Supervisors:   Dr Stefan Iglauer & Dr Andrew King
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Jonathan Goodwin
(2011/2012)
Alumn Year:   2011/2012
Visualisation Software for 2-particle Discrete Quantum Walks
https://vimeo.com/showcase/3323013/video/123394673
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Maryam Masoum
(2011/2012)
Alumn Year:   2011/2012
Understanding the Interactions of Complex Carbohydrates with Proteins using Molecular Docking
https://vimeo.com/showcase/3323013/video/123393859
Click here to view Maryam's presentation
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Ran Li
(2011/2012)
Alumn Year:   2011/2012
Do machines have emotions? Just like…
https://vimeo.com/showcase/3323013/video/123591489
Click here to view Ran's presentation
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Steven Murray
(2011/2012)
Alumn Year:   2011/2012
A Tomography for Radio Astronomical Data Cubes
https://vimeo.com/showcase/3323013/video/123393858
Click here to view Steven's presentation