Our Students



Adam Drogemuller is a PhD Student at the University of South Australia specializing in topics of Human-Computer Interaction and Virtual Reality. His research emphasizes on investigating approaches on how to improve the communication of data by making it more engaging and accessible through the use of novel and immersive technologies. In particular, interactive data visualizations through Virtual Reality and physical fabrication.

Publications: Google Scholar

Tags: Data Visualization | Immersive Analytics | Design | Fabrication


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  • Alasdair is a PhD student in the Computation Media Lab at the Australian National University. His research sits at the intersection of computer vision, natural language processing, and neural graph networks. He’s particularly interested in encoding knowledge into deep learning models. Prior to his PhD studies, he worked as a data scientist and a software engineer at various organisations and startups including Data to Decisions CRC and Mathspace.
  • Publications:  Google Scholar
  • Tags: Natural Language Understanding | Image Captioning | Graph Neural Networks | Computational Social Science
  • Highlight: I love working at the forefront of machine learning and being able to build machines that can think more like humans. It’s amazing to see how much progress we’ve made in recent years in deep learning and I’m really curious to see what an AI in 10 years would look like!
  • Anahita is a PhD scholar at UNSW and DATA61 CSIRO Australia. The driving interest of her research is around adversarial machine learning. She has hands on experience using big data analytics tools and in-depth technical knowledge of data science particularly in machine learning, statistical analysis, credit scoring and customer segmentation. her current research interests encompass adversarial machine learning, Artificial Intelligence, IoT, and cybersecurity.
  • Publications: Google Scholar
  • Tags: Adversarial Machine learning | IoT
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  • Bushra is a Data61 PhD student from the University of Adelaide working on Adversarial Machine learning in the Cyber-security domain. Specially she is working on evasion and poisoning attacks and defences in Cyber-security systems such as phishing, spam, malware, intrusion and data exfiltration detectors. Previously, she worked on two studies after commencing her PHD which are under review.
  • 1. Machine Learning for Detecting Data Exfiltration: A Review (ACM Computing Survey)
    2. Impact of Adversarial Examples on Phishing URL Classifiers (IEEE symposium of Security and Privacy)
  • Publications: IMTDE
  • Tags: Adversarial Machine Learning | Deep learning | Cybersecurity
  • The focus of Chadni’s PhD is providing reference architecture for security orchestration and automation that can facilitate design and development of concrete self-adaptive security orchestration platform. Security orchestration and automation aim to integrate different types of multivendor security software used by a security operation centre to automate and accelerate the incident response process. Her work falls at the intersection of cybersecurity and software engineering that aims to provide architecture support for large-scale realisation of security orchestration and automation. Chadni is leveraging existing software engineering, analytical reasoning, natural language processing and machine learning tools and techniques to develop an intelligence self-adaptive security orchestration and automation platform.
  • Publications: Google Scholar
  • Tags: Software Engineering | Software Architecture | Cybersecurity | Security Orchestration and Automation | Natural Language Processing
  • Highlight: I find that attempting to address one of the critical issues faced by security operation centres – integrating multivendor isolated heterogeneous security software/tools – incredibly motivational. Mentoring has always played an essential role in my PhD Journey, thats why working under the direct supervision of prominent researchers, such as Dr Surya Nepal and Professor Ali Babar, inspires me to improve my capabilities on a daily basis.
  • Chaoran received a Bachelor of Information Technology degree from Deakin University in 2018. He is currently working towards a PhD degree at Swinburne University of Technology. His research interests include malware detection and adversarial machine learning. Deep learning techniques have recently been developed to cope with the booming increase of malware in various platforms, rendering investigating vulnerabilities and enhancing the robustness even more critical.
  • Publications: Google Scholar
  • Tags: Malware Detection | Adversarial Machine Learning
  • Charles is a PhD student at CSIRO’s Data61 researching distributed self-managing systems. Charles’s research is focused on creating more resilient and robust networks by decentralising existing network control models. Previously, Charles worked as a specialist for one of the largest (physical) metrology equipment suppliers in Australia, applying his expertise in a number of diverse engineering domains including aerospace, automotive, rail, defence, medical and power generation.
  • Publications: Google Scholar
  • Tags: Multi Agent Systems | Distributed Reasoning | Distributed Self-Managing Systems
  • Highlight: Since first learning about programming and AI in my early twenties, I have been working to transition my career. Now, working with world leading experts, I am truly inspired to apply myself in addressing the challenges of the future.
  • Chehara is a PhD Candidate at the Faculty of Information Technology, Monash University Melbourne where she is part of the Cyber Security and Systems research organisation. She is bringing hands-on background experience in data mining, machine learning and image processing technologies. The main objective of her research is to preserve building automation data and user behavioural patterns from outsiders as building automation systems share their sensitive data with stakeholders to gain benefits or reduce overall cost. Therefore, by creating an interface to protect the smart building data, it is possible to avoid tenants being identified, profiled and tracked.
  • Tags: Data Mining | Cyber-security and Privacy | Image and Signal Processing | Machine Learning
  • Highlight: Being surrounded by people who are just as keen on cybersecurity as I am, and understanding the competitiveness of the discipline is motivating me to progress even more.


David is a PhD candidate at the UNSW Sydney and Cybersecurity CRC. His current research interests lie in developing machine learning techniques applied to Cybersecurity. Existing research focuses on graph neural networks to improve existing deception capabilities across various forms of media. David has also spent several years working in industry as a commercial manager and machine learning engineer within Australian and international technology companies. 

  • Publications: Orcid
  • Tags: Machine Learning | Graph Neural Networks | Cybersecurity | Deception Technology
  • Feixue received a Master of Software Engineering Degree from the Zhejiang University of China. She currently is a Data61 PhD student from the Swinburne University of Technology, Australia. The focus of Feixue’s PhD is providing interpretability for cyber-security systems, related to AI. She is engaged in explaining complex and obscure neural networks. Previously, she worked on a critical functional department, with her specialty of engineering such as software design, architecture development and system reconstruction.
  • Publications: dblp |Google Scholar
  • Tags: Interpretability | Software Engineering | Machine Learning
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  • Huihui is a PhD candidate at Faculty of Engineering and Information Technologies, the University of Sydney, under the supervision of Dr. Chang Xu and Dr. Siqi Ma (CSIRO’s Data61). His main research areas are machine learning and adversarial examples. Specifically, he is working at how to improve the performance and robustness of deep neural networks.
  • Publications: Google scholar
  • Tags: Machine Learning | Adversarial Examples


  • Kane is a Computer Science PhD candidate at UNSW working with The Cyber Security Cooperative Research Centre on security of distributed machine learning. Previously completed a Bachelor of Computer Science (1st class) at UNSW which culminated in a thesis on satellite image retrieval using unsupervised machine learning models. Kane began his career in the healthcare setting but developed a passion for deep learning and is now dedicated to ensuring Australia can use this amazing technology in a safe and secure way.

Publications: orcid

Tags: Machine Learning | Cybersecurity | Federated Learning

  • Keelan is an Honours student at Edith Cowan University and collaborating with Data61. Currently, he is working on using machine learning techniques to detect spear phishing attacks. His research interests involve using artificial intelligence for security.
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  • Tags: Machine Learning | Phishing | Cyber Security

Highlight: I am excited to be a part of Data61 and be able to research cutting-edge AI technologies.

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  • Le is a PhD student at the University of Wollongong (UOW), sponsored by a CSIRO’s Data61 scholarship. In 2011, he received his Master Degree in Mathematics from Ho Chi Minh City University of Science, Vietnam, after a bachelor at the same university. From 2007 to 2016, he worked as a mathematics teacher in high school. Le also spent time as a research student in Japan. His current research interests include post-quantum cryptography, focusing especially on lattice-based cryptography.
  • Publications: Google scholar
  • Tags: Post-quantum Cryptography | Lattice-based Cryptography
  • Highlight: I am very grateful to CSIRO’s Data61 and respectable professors at both CSIRO’s Data61 and University of Wollongong for giving me a wonderful chance to work with them, helping me to push my passion and my research up.
  • Lihong received her Bachelor degree with Honours from Deakin University, Australia. She is currently a PhD student with CSIRO’s Data61. She is working on Android malware detection and evolution analysis. Her research interests include Android malware analysis, adversarial attacks under the Android context, human-centric research, and explainable machine learning. Understanding the evolution of Android malware facilitates the development of defending techniques by proactively capturing the features of Android malware. This work is aimed at supporting comprehensive modelling on malware’s evolution and providing a detailed explanation of the reason why Android malware evolved in those ways.
  • Publications: ORCID
  • Tags: Android Malware | System Security
  • Lu Yang is a PhD Candidate and a Sessional Teaching Staff in the Queensland University of Technology. He is a Domestic Full-Time Australian Government RTP Scholarship holder and a CSIRO Data61 Student Scholarship holder. In 2016, he received a Bachelor of Electronic and Communication System Engineering from the Australian National University. In 2018, he received a Master of Networking and Communication Engineering from the Queensland University of Technology. In the last year of the Bachelor of Engineering study, he worked in the Optical Biofluidics Imaging Group in the Australian National University. In the Master of Engineering study, he worked in the Inorganic Nanomaterials Lab in the Queensland University of Technology. He specializes in signal processing methods and electronic system design. In the PhD study, his main areas of research interests include Internet of Things, Physical Layer Security, and Communication Theory.
  • Publications: ORCID
  • Tags: IoT | Physical Layer Security | Communication Theory


  • Maisie is a PhD student from UNSW, researching automation techniques to support conducting empirical studies (such as systematic reviews) across different research disciplines. Maisie’s research is focused on adopting machine learning (especially NLP) and crowdsourcing methods to mitigate significant challenges that researchers are facing when conducting these types of studies and dealing with the huge amount of unstructured data.
  • Tags: Insight Analysis | Big data analysis | Crowdsourcing | Empirical studies | Design Thinking 


Mark is a PhD student at the University of South Australia. His research focus on further understanding Fake News and other deceptive online content on social media, and how it can be detected and prevented. In particular, he’s interested in examining trends and propagation of deceptive content and its spread on the right wing and populist areas of the social media website, Reddit.

  • Publications: Google Scholar
  • Tags: Machine Learning | Big Data | Data Science | Social Media | Fake News



  • Michael is a PhD student from the University of Adelaide focusing on binary vulnerability analysis using fuzzing techniques. He is particularly interested in manipulating and executing binaries in order to analyse them in new and unique ways.
  • Publications: Google Scholar
  • Tags: Cyber Security | Fuzzing | Emulation | Virtual Machine


  • Mohammad is a PhD student at Monash University, sponsored by a CSIRO’s Data61 scholarship. Formerly, he developed essential skills to be prepared for commencing a PhD by working around 8 years in industry, 3 years in academia and disseminating research findings in leading venues, simultaneously. Mohammad is currently pursuing his PhD at the Faculty of Information Technology at Monash University, under the supervision of Dr. Adel Nadjaran Toosi and Dr. Raj Gaire. Motivated by his passionate for expanding existing knowledge of distributed systems, his research is intended to extend the elasticity of cloud computing to the edge of the network to improve the performance of IoT/Smart applications and Microservices. Previously, he developed elasticity-related optimizations in cloud, but the emergence of Edge/Fog Computing and 5G networks made him enthusiastic about bringing the elasticity benefits to the edge to improve the Quality of Service (QoS) from both service providers and end-users’ perspective.
  • Publications:  Google Scholar; Publons; Research Gate; LinkedIn; ORCID
  • Tags: Distributed Systems | Cloud Computing | Edge/Fog Computing | Serverless Computing | Autonomic Computing
  • Highlight:  Reaching realistic solutions for challenging real-life problems in IT domain is what I am eager for. To be fulfilled, I am always looking for a chance to collaborate with not only CSIRO’s Data61 and Monash University researchers, but also other talented, experienced and like-minded researchers in the distributed systems community around the world. Working with people smarter than I always helps me to learn and make encouraging progress.


  • Nauman is a PhD research student at the School of Science, RMIT University, Melbourne, Australia, and a researcher at CSIRO’s Data61.  He is working on lightweight cryptographic protocols for the Internet of Things, looking at communication security and cryptographic schemes to maintain the confidentiality of data at smart nodes as well as during communication between smart devices or with the cloud platform.  He worked on an SNMP based cybersecurity monitoring system during his Masters.  He also worked on MPI programming to optimize the Galois/Counter Mode (GCM). His research interests include network security, Internet of Things security, digital forensics and cryptography.
  • Publications:  Orcid , Google Scholar , LinkedIn
  • Tags:  IoT | Security | Lightweight Cryptography | Network Security | Digital Forensics
  • Robert worked as a Research Assistant at QUT in 2019 investigating blockchain within industry application. Currently Robert is undertaking a PhD degree with QUT, CSIRO, and Data61 researching “Distributed and Privacy Preserving Analytics of Smart Grid Data”. Robert has over a decade of hands on experience in the ITC industry, including working for 5 years as the Network Administrator for the Statistical Institute of Belize.
  • Tags: Machine Learning | Blockchain Technology | Cybersecurity


  • Roelien is a Cyber Security PhD candidate based in Sydney. Her research focuses on quantifying and optimising the performance of Cyber Deceptions. Her research will utilise modern techniques such as natural language processing and simulation testing to determine methods to compare Cyber Deceptions such as honey files. Previously, Roelien worked as a Research Analyst at the European Central Bank in Frankfurt. She holds a Master in Econometrics from the University of Amsterdam.
  • Tags: Semantic Vector Space | Decoy Documents | Cyber Deception | Characteristic Metrics | Cyber Security | Honey files | Natural Language Processing | Data Science
  • Highlight:  I am thrilled to combine my love for coding and statistics to thwart espionage.
  • Rongjunchen is a junior PhD student at CSIRO’s Data61. Rongjunchen Zhang received his Bachelor of Science degree in Software Engineering from both Southwest University, China and Deakin University, Australia in 2018. In the same year, he received a Bachelor of Science (Honours) degree with first-class honours from Swinburne University of Technology. He began his PhD candidature in April 2019 at Swinburne University of Technology. His main research interests are natural language processing, reinforcement learning, and security of chatbot.
  • Tags: Software Engineering | Machine Learning | Reinforcement Learning
  • Sami is a PhD student in the Department of Computing and Information Systems at Melbourne University. Sami is interested in studying users privacy in emerging technology applications in the health industry, such as Internet of Things, machine learning and AI. He is also a candidate of the Australasian College of Health Informatics (ACHI) Fellowship by Training Program (FbT) for health informatics research doctorate students. Sami has a career of 15 years and has held various positions in the field of IT and telecommunication value-added services.
  • Publications: ORCID |Researchgate |Google Scholar
  • Tags: Privacy | Internet of Things | Health Informatics | Emerging Technologies


Samudra is currently a PhD student in mathematics and computer science at the University of Adelaide. She is interested in developing entity resolution (ER) methods to handle big data. Accurate and efficient ER has been a problem in data analysis and data mining projects for decades. ER is an important, required step in data integration when identifying a group of entities (records) representing the same real-world entity in multiple databases. With the advent of big data computations, the demand for scalable ER techniques has increased and she is looking at new challenges big data brings in to the context of ER.

Tags: Entity resolution | Data integration | Data linkage | Data analysis

Highlights: The most exciting thing in doing research is to have the flexibility of pursuing new ideas at the same time as contributing to knowledge. As my advisors always say PhD is a rollercoaster. I have good days and bad days but the journey goes on and I am passionate.

  • Sara is currently a PhD student in the Faculty of Information Technology, Monash University, Australia. She received her Bachelor and Master degrees in Electrical and Electronic Engineering from the Sharif University of Technology in 2011 and 2013 respectively. Saras research interests include deep neural networks, physical-layer network coding and applications of lattices in wireless communication.
  • Tags: Deep Learning | Wireless Communication | Coding Techniques | Lattices
  • Sunil is a proficient software engineer with more than three years of industry experience in software development. In his PhD, he is leveraging his experience to design and build an adaptive resource management framework for cloud hosted IoT data pipelines. Sunil aims to pursue a career as a software research engineer focusing on designing and building high performance software tools and frameworks for distributed systems. He proposes a novel resource allocation model for finding the QoS assured and cost-effective cloud resource for IoT data pipelines. It captures the end-to-end QoS requirements of the pipeline while reducing the total cost of ownership and achieves the state-of-the-art performance in terms of the cost-effectiveness and quality requirements. Sunil also proposes a novel resource management framework for an adaptive management of cloud resources utilising the autonomic computing paradigm and optimisation technique for cloud hosted IoT data pipelines. The framework defines and captures the notion of sustainable QoS, including the IoT data ingestion benchmarks during the adaptive resource management process. In addition, the framework considers the challenges in IoT data analytics such as data volume, velocity, and latency requirement.
  • Publications: Google Scholar |researchgate
  • Tags: Autonomic Computing | Optimisation | Cloud Computing | Big Data Pipeline | IoT
  • Tom is currently a machine learning researcher with a current focus on adversarial machine learning in natural language processing. In a previous life, Tom worked as a data scientist building systems for Telstra, Caltex and Woolworths. He enjoys solving problems, building things, mathematics, writing, and long walks on the beach. You can learn more about Tom at tomroth.com.au
  • Publications: none yet! (but hopefully soon)
  • Tags: Adversarial Machine Learning | Robustness | Reinforcement Learning | Natural Language Processing
  • Highlight: I like breaking a problem down to the fundamentals and making solutions as simple as possible.
  • Viet is pursuing a PhD degree in Information Technology Faculty at Monash University. His research focuses on cybersecurity and crypto-applied studies.
  • Publications: Google Scholar
  • Tags: Data Privacy | Cybersecurity | Computational Science
  • Xiaogang is a PhD student at the Department of Computer Science and Software Engineering, Swinburne University of Technology. He got his Masters degree at Xian Jiaotong University. His expertise is in detecting vulnerabilities utilising fuzzing. Fuzzing is a popular technique utilised for finding software bugs. Famous fuzzing tools such AFL and OSS-FUZZ have found thousands of bugs in real world applications. His other research interests are system security and IoT security.
  • Publications: Google Scholar
  • Tags: Fuzzing | System Security | IoT
  • Xu’s research interests include secure authentication in Internet of Things (IoT), IoT security and applied cryptography.
  • Publications: ORCID id |Google Scholar
  • Tags: IoT | Cryptography | Authentication | Privacy | Security
  • Highlight: I get excited by doing research that I think can significantly improve our daily life.
  • Olivia Shen is a first year PhD student at the Faculty of Information Technology, Monash University with research interests in searchable encryption, encrypted database, and privacy preserving crowdsourced data analytics in the broad Cyber Security subject domain.
  • Tags: Metadata Privacy | Searchable Encryption | Encrypted Database | Crowdsourcing | IoT
  • Yannan’s research interests include secure cloud storage and blockchain-based applications as she is aiming to address the privacy and security issues in blockchain with the cryptographic tools and find new blockchain-based applications.
  • Tags: Blockchain | Smart Contract | Solidity | Cloud Storage | Data Auditing
  • Highlight: Blockchain is a revolutionary topic that is fully decentralised. I enjoy working on such an emerging and challenging topic as my research interest. I like cryptography, which is mysterious and magical. Solving problems with crypto tools makes me happy and fulfilled. Talking to people about my research helps me a lot. I like to discuss my research topic with not only my supervisors but also my classmates and colleges, who will always inspire me a lot.
  • Zian Liu is a first year PhD. student. His research focuses on Malware analysis in Cyber Security.
  • Tags: Malware Analysis | Cyber Security
  • Zichan is an HDR scholarship holder at CSIRO’s Data61. She received her Honors and Bachelor degree in Information Technology at Deakin University. Motivated by her passion for Cybersecurity and programming, she involved in different projects ranging from visualization for Cybersecurity to anomaly detection using graph embedding. Her current research interests are intelligent anomaly detection and Software Defined-Network security. Her main techniques are machine learning algorithms and graph embedding algorithms.
  • Tags: Anomaly detection | Machine Learning | Graph Embedding | Graph Neural Network
  • Highlight:  Step out of your comfort zone.