Our Students

Baiqi Chen | University of Queensland

Baiqi Chen is a PhD candidate at the University of Queensland and CSIRO’s Data61, under the supervision of A/Prof. Guangdong Bai. Her main research areas are human-centric cyber security and security usability. Baiqi’s PhD project explores the human-centric factors to inform the development of a framework for designing accessible and effective technology-mediated cyber security nudges. Specially, Baiqi investigates the use of nudges in the domain of Virtual Personal Assistant (VPA) applications and websites for diverse end-users in a variety of cyber security decision-making contexts.
Publications: Orcid

Ben Harper | University of Melbourne

Ben is a PhD student at the University of Melbourne. He received a BSc. and MSc, both majority in Physics at the University of Melbourne. His Masters research project was on classical simulation of quantum computation, using stabilizer simulation techniques, and using this simulator to implement quantum error correcting codes. Now for his PhD project, Ben is working to develop a quantum compiler – taking high level code and transforming it into something that can be run on physical machines.
Tags: Tags: Quantum Computing | Compilers | Quantum Error Correction | Simulation

David James Holmes | Edith Cowan University

David is a Cyber Security CRC PhD student from the Edith Cowan University (ECU) in Perth, Western Australia. David’s PhD project relates to the semantic modelling of digital twins from a cyber security defence automation perspective. David also has an interest in the blending of blockchain technology into the digital twin and cyber-physical system domains.  David comes to the research environment with a Master of Network Technology, a Master of Cybersecurity and extensive experience in private enterprise, where, as a network engineer, he worked for over 25 years in Australia and in other countries around the world providing business end-user network technical support.
This business experience gives David a unique perspective on the mutually beneficial returns from the interaction between research and commerce where his focus is on making research relevant to the business model.
Publications: Orcid: https://orcid.org/0000-0002-8644-4387
Tags: Network Engineering | Honeypots | Semantic Modelling | Digital Twins | Blockchaining

Erik Buchholz | UNSW

Erik is a Computer Science PhD candidate at UNSW Sydney and is working with the Cyber Security Collaborative Research Centre. His research focuses on the question of how collected data can be used safely for research purposes without harming privacy, e.g., preventing the re-identification of individuals from a shared dataset. Before coming to Sydney, Erik completed both his bachelor’s and master’s degree at the RWTH Aachen in Germany. Ever since his first seminar during his bachelor’s degree, Erik worked on projects that aim at the improvement of user privacy. For his bachelor’s thesis, he developed a platform that gives the users control over their own data and the decision of who is allowed to utilize it. In his master’s thesis, Erik developed a prototype for a privacy-preserving exchange platform that allows exchanging process parameters in industrial settings.

Personal Bio Website: https://erikbuchholz.de/
Publications: Google Scholar
Highlights: Personal privacy is a human right, and privacy protection plays a crucial role in today’s interconnected world. I am very excited to help finding solutions that allow the protection of individuals’ privacy without preventing sensible utilization of data and without hindering progress. By protecting everyone’s privacy, I want to contribute to making this world a safer and better place.
Tags: Privacy-enhancing Technologies | Cybersecurity | Sensitive Information Sharing

Falih Febrinanto | Federation University Australia

Falih is currently a Ph.D. Student at Federation University Australia, Ballarat Campus and working under the supervision of Dr. Kristen Moore (Data61), Dr. Chandra Thapa (Data61), and A/Professor Feng Xia (FedUni). His current research focuses on deploying graph learning and lifelong learning for human-centric applications. The activities include enabling the learning agent to learn continuously for addressing incremental tasks of graph data in a specific domain of misinformation and malicious activities.
Publications: https://scholar.google.com.au/citations?user=wqzYPTcAAAAJ&hl=en&oi=ao
Tags: Graph Learning | Lifelong Learning | Deep Learning | Human-Centric Cyber Security

Geetanjli Sharma | La Trobe University

Geetanjli pursued a Master of Information Technology degree from La Trobe University, Australia. She is a PhD candidate at the La Trobe University, Australia. She has spent couple of years as Software Developer in Amdocs, India. Her experience includes working on telecommunications and DTH projects that involves optimizing the services for better performance and high customer satisfaction. Her research interests focus on detailed study and mitigations of cyber security issues in different sectors i.e Health, Agriculture and Fintech using Distributed Machine Learning.
Tags: Federated Learning | Distributed Machine Learning | Cyber Security

Gnanakumar Thedchanamoorthy | Charles Sturt University

Gnana is a PhD student attached to the School of Computing & Mathematics, Charles Sturt University. His interests are in privacy preserving data collection techniques for statistical aggregation. Currently his area of interest lies around Local Differential Privacy. He is being supported by a scholarship from the Cyber Security Corporate Research Centre.
Publications: Orcid ; Researchgate ; Google scholar;
Tags: Data Privacy | Differential Privacy | Local Differential Privacy.

Hetong Jiang | University of Queensland

Hetong is a first year PhD student at The University of Queensland. He received his bachelor degree majoring in mechanical engineering from the Queensland University of Technology, Australia in 2018, and Master of IT degree from The University of Queensland, Australia in 2020. His research interests include data provenance, distributed Storage and any cyber security-related areas.
Publications: https://orcid.org/0000-0003-4149-6244
Tags: Data Provenance | Distributed Storage | Cyber Security

Hevish Cowlessur | University of Melbourne

After nearly 3 years of experience as an Electrical Engineer in the industry, I am currently doing a Ph.D. in Quantum Machine Learning with applications in Cybersecurity at The University of Melbourne and CSIRO/Data61. My experiences over the years have enabled me to develop skills in leadership, problem-solving and decision-making. I intend to use these to bring a fresh perspective to my current research endeavours and make significant contributions to the field of Quantum Technologies.

Hossein Rahimpour | UNSW

Hossein Rahimpour is a PhD student at UNSW and CSIRO’s Data61. He is the recipient of the Australian Government Research Training Program (RTP) scholarship as well as the Cybersecurity Cooperative Research Centre scholarship 2021-2024. Hossein is a Chartered Professional Electrical Engineer and holds Mphil degree in Electrical Engineering from the University of Newcastle. He worked over 15 years in various roles in power industry with expertise in high voltage, transformers and asset management. Hossein’s PhD project explores the application of AI techniques in cyber protection of smart transformers in modern electrical network. The continuing digitisation of power grid and the evolving cyber-attacks motivated him to pursue a higher degree research in this area.
Tags: Cyber Security | Artificial Intelligence | Smart Grid | Smart Transformers

Jeff (Haodong)Lu | University of New South Wales

Jeff (Haodong) Lu is currently pursuing his PhD at the University of New South Wales (UNSW). He is an alumnus of the same institution, where he earned both his Bachelor’s degree and Honors in Computer Science. Jeff’s research is primarily centered around the development of reliable machine learning models and identifying potential vulnerabilities within these systems. He has a particular interest in the detection of out-of-distribution instances across a range of problem domains. His work is especially focused on deep learning models and the robustness of these models when faced with adversarial or abnormal samples. His dedication to enhancing the trustworthiness and resilience of machine learning systems is a testament to his commitment to the field.
Tags: Deep Learning | Trustworthy Systems
Publications: Orcid

Jialiang (Kaelin) Dong | University of New South Wales

Jialiang is a PhD Student at UNSW Canberra. His current research focuses include on-device AI security, software security, and software supply chain security, encompassing areas such as AI model deployment analysis, third-party library analysis, and static analysis. In addition to traditional software analysis techniques, he is actively exploring the application of AI tools in software analysis.
Tags: Software Security | AI Security | Software Supply Chain Security
Publications: Google Scholar

Jiamin (Rhea) Chang | University of New South Wales

Jiamin (Rhea) Chang is a Ph.D. student at the University of New South Wales and Data61, specializing in Trustworthy Generative AI research. Prior to commencing her PhD, she completed a Bachelor of Computer Science (Hons) at the University of of New South Wales. Jiamin’s research focuses on the reliability of generative AI, including compilation of Unreliable Generated Samples from AIGC and aim to the establishment of novel and reliable AIGC.
Tags: Security in AI | AIGC | Muti-model
Publications: Google Scholar

Keerth Rathakumar | ANU

Keerth is a research affiliate of CSIRO. He gained his Bachelor of Science from ANU and worked in the analytics and software engineering industry before joining CSIRO. His research interests lie in representation learning. In particular, he is interested in interpretable generative models.
Tags: Generative models | Inference

Kevin Saric | QUT

Kevin is a commercially-minded QUT PhD candidate with over 20 years of experience in a diverse range of fields. These include cloud services (before it ever had such a name), banking, public health, energy, mining, and cybersecurity. He has the unusual distinction of having worked for 4 of CSIRO’s major business units in various capacities. He enjoys all things technical and reads as much as reasonably possible, and then some more.

Larry Huynh | The University of Western Australia

I am a PhD student at the University of Western Australia, looking into developing quantum-inspired machine learning algorithms for improving cybersecurity. My research interests lie in this intersection of classical machine learning and quantum computing, and exploring the possible advantages that can be derived from quantum mechanics and distilled into classical computing models. I’ve also gained experience in various applications of classical machine learning. My work includes NLP for rumour generation to better combat the limitations of rumour detection models, anomaly detection techniques for maritime contexts with limited available data, and enhancing methods for visual field defect classification using perimetry data.
Publications: Google Scholar
Tags: Machine Learning | Quantum Computing | Quantum-Inspired ML | Cybersecurity
Highlight: I’m passionate about the transformative power of computer science education. As an educator, I aim to inspire and empower the next generation of software engineers and cybersecurity experts, helping them build the skills and knowledge they need to make their mark in this exciting field.

Mathew Duong | The University of Adelaide

Mathew is a master’s student at the University of Adelaide. He received his bachelor’s degree in software engineering at the University of Adelaide in 2021. His research is centered in cyber security specially looking into vulnerability analysis using automated testing techniques.
Tags: Cyber Security | Fuzzing | Causality analysis

Mengyao Ma | The University of Queensland

Mengyao is a PhD student at the University of Queensland (UQ) and is working with the UQ TrustLab. She received M.DS. degree at UQ. Her current research interest lies in Trustworthy AI within Distributed frameworks, under the supervision of A/Professor Guangdong Bai.
Publications: Google scholar
Tags: Trustworthy AI | Federated Learning | Distributed Machine Learning

Michael Chesser | The University of Adelaide

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

Mina Khan | The University of Adelaide

My name is Mina Khan. I am a PhD candidate at Deakin University, Australia. My research focuses on developing a useability framework for Verifiable Credentials which will help promote the use of Decentralized Identity Management solutions. I am studying under the supervision of Prof Robin Ram Mohan Doss (Deakin University), Dr Jay Jeong (Deakin University), Dr. Marthie Grobler (CSIRO/Data61), Tina Wu (CSIRO/Data61), Jasmin Krapf (BUPA) and Paul Burrow (BUPA). Previously, I completed my Master of Information Technology from Charles Sturt University, Australia in 2018. The title of my final year project was “Internet of Things (IoT) From a Legal and Ethical Perspective”. I completed my Bachelor of Science in Electrical (Computer) Engineering from COMSATS Institute of IT, Pakistan in 2016. The title of my final year project was “IP Controlled Robot Receptionist/Guide through Raspberry Pi”. During my PhD, I am eager to learn from all my supervisors and just like them, become an expert in the field of cybersecurity.
Tags: Verifiable credentials | Decentralized Identity | Self-sovereign identity (SSI)

Nam Tran | University of Wollongong

I am currently a PhD student in Cryptology from the University of Wollongong, under the supervision of Prof. Willy Susilo and Dr. Khoa Nguyen. Before that, I received a master’s degree in Cryptology in the University of Limoges (France) and spent 6 months as a research intern in the Institute of Mathematics of Bordeaux (France). My research aims at providing a better understanding about the current state of post-quantum cryptography, namely the theoretical foundation and connection between lattice-based and code-based cryptography, and possibly how they could be adapted to a quantum setting.
Publications: Google Scholar; Publons; Research Gate; LinkedIn; ORCID
Tags: Lattice-based cryptography | code-based cryptography

Nikai Jagganath | Monash University

Nikai is a PhD student in the Department of Software Systems and Cybersecurity at Monash University. She is a recipient of CSIRO’s Data61 PhD Scholarship as part of the Quantum Technologies Future Science Platform (QT-FSP). After being captivated by cryptography during her BSc in Mathematics and Computer Science at the University of Cape Town, she continued to pursue her studies in this field and in 2021 sealed her MSc in Mathematics of Cryptography and Communications from Royal Holloway, University of London. Her research interests include post- quantum cryptography, specifically lattice-based cryptography, and how it can be integrated with quantum cryptography. She will be under the supervision of Dr R. Steinfeld, Dr A. Sakzad and Dr M.F. Esgin from Monash University and Dr D. Liu from CSIRO during her PhD programme.
Publications:

ORCID record https://orcid.org/0000-0002-2155-3179.
Tags: Post-quantum cryptography | Quantum cryptography
Highlight: I feel privileged to have this opportunity to work with such esteemed academics in my field of cryptography (and to explore a new country!). I am keen to contribute to an area I believe is critical in this Information Age when the threat posed by quantum computers is ever-looming.

Nuwan Kaluarachchi | RMIT University

Nuwan Kaluarachchi is an HDR candidate in mathematical sciences at RMIT. He is working with Multimodal multi-device continuous authentication to enhance safety by adding more protection over passwords and PINs to multiple devices simultaneously using machine learning and deep learning techniques under the supervision of Dr Arathi Arakala (RMIT), Dr Sevvandi Kandannarachchi (RMIT) and Dr Kristen Moore (Data61),. He completed his bachelor’s degree in Applied Statistics at the Faculty of Science, the University of Colombo, with a First Class Honours. He won the Gold Medal for the Best Student in Applied Statistics. He worked as an Associate data engineer at Altria Consulting Pvt. Ltd before arriving in Melbourne, Australia.
Tags: Use authentication | Biometrics | Machine learning algorithms | Anomaly detection
Highlight: ‘Digital security is essential in the highly digitalised world. I’m excited to support finding better solutions for providing a safe multi-device experience by enhancing the social well-being of the users. Your device is under your complete authenticity.’

Robert Marlin | Queensland University of Technology

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

Saud Khan | The Australian National University

Saud is a Doctoral Candidate with the School of Engineering at The Australian National University and Data61, CSIRO. He holds a first-class honours equivalent qualification and receives the equivalent Australian Government Research Training Program and CSIRO Postgraduate Research Scholarship. His previous background includes two years of full-time experience overseeing industrial project research at the Kumoh National Institute of Technology, South Korea, funded by the National Research Foundation of Korea (equivalent to Discovery Project of the Australian Research Council). His research focuses on bridging the gap between deep learning, the internet of things, and cybersecurity in the physical layer wireless communication domain.
Publications: https://scholar.google.com/citations?user=eEe1-SkAAAAJ&hl=en
Tags: Deep Learning | Internet of Things | Wireless Communication | Physical Layer Security

Shane (xinguo) Feng | University of Queensland

Shane is a PhD Candidate at University of Queensland and CSIRO’s Data61, under the subversion of A/Prof Guangdong Bai and Dr Sharif Abuadbba. His research interests include Trustworthy Machine Learning and Natural Language Processing. His existing research focuses on exploring the privacy issues of Large Language Models under Federated Learning setting.
Publications: Orcid
Tags: Machine Learning | Natural Language Processing | Trustworthy ML

Shangzhi Xu | University of NSW

Shangzhi earned his Honors degree in 2023 and is now a Ph.D. student supervised by both UNSW Canberra and CSIRO. His current research interests lie in software security, including 0-day and 1-day vulnerability detection with static analysis and fuzzing. More specifically, he is working on IoT security, C/Cpp language security, memory-related vulnerability detection as well as the interaction between traditional analysis and state-of-art AI.
Highlight: ‘My passion lies in coding and innovation, where creating new solutions to existing problems thrills me.’

Shuofeng Liu | The University of Queensland

Shuofeng Liu is a PhD student at the University of Queensland and Data6,1specializing in Trustworthy AI research. With a primary focus on Natural Language Processing (NLP), Shuofeng’s work revolves around exploring various aspects of NLP and its applications. His research interests encompass data privacy protection, NLP techniques, and computer vision. Shuofeng is passionate about the development of AI technologies that not only deliver accurate results but also prioritize privacy and fairness. By addressing the challenges in natural language understanding, sentiment analysis, and machine translation, he aims to contribute to the responsible and ethical deployment of AI systems across different domains.. Shuofeng remains dedicated to advancing the field of Trustworthy AI. With a strong foundation in NLP and a multidisciplinary approach, they strive to make meaningful contributions to the scientific community. By leveraging their expertise and collaborating with peers, Shuofeng aims to drive the progress of AI research, promoting its societal impact and ensuring the development of reliable and trustworthy AI systems.
Publications:Orcid
Tags: Trustworthy AI, NLP, CV, Deep Learning
Highlight: Shuofeng has already designed a Algorithm-specific encryption method to protect the original data from being leaked, called AlgoSpec. He is also extremally interested in trustworthy AI, especially in LLM, NLP and time-series data analysis domains. And now he devotes himself in researching how to build a task-specific LLM, for the sake of protecting the original input, and also finding a way to detect the AI-generated text.

Tina Moghaddam | University of Queensland

Tina is a PhD candidate at the University of Queensland and CSIRO’s Data61. Her research is focused on investigating AI-powered attacks on networks and developing novel defences against these attacks. Prior to commencing her PhD, she completed a dual Bachelor of Software Engineering (Hons) and Science (Physics) at the University of Queensland, Australia.
Publications: Orcid | Google Scholar
Tags: Cybersecurity | Network Security | Machine Learning | Moving Target Defence
Highlight: As AI technologies proliferate, they’re starting to be used in malicious ways. Protecting against these is a new and important challenge. I’m very excited to be working to develop defences in such a fast developing field.

Xinyu Zhang | Monash University

Xinyu is a first year PhD candidate in the department of software and cybersecurity at Monash University, whose research is sponsored by Data61 CSIRO. Xinyu’s research focuses on developing post-quantum multiparty signatures based on symmetric primitives (e.g., hash functions, pseudo-random functions, and block ciphers). She is passionate about cryptography, especially digital signatures, and variants (i.e., signatures involving multiple signers). Currently, she is working on constructing post-quantum multi-signature protocol utilising symmetric primitives only. She is also interested in applying the protocol to blockchain-based applications such as cryptocurrencies and blockchain-based supply chain systems.
Publications: Google Scholar
Tags: Cryptography | Post-Quantum Cryptography | Symmetric Primitives | Blockchain

Younghun Kim | University of Melbourne

My passion lies in realizing the full potential of quantum computers to address practical challenges and capitalize on quantum advantage. I’m dedicated to minimizing errors in quantum devices and developing effective strategies to achieve this objective. I have designed and optimized quantum circuits to reduce errors in the system, tested them on one of the real quantum processors built by the IBM company, and managed to see error suppression. Presently, my focus is on assessing the performance of a quantum logic gate, with a primary emphasis on ensuring its robustness against errors.

Highlight: ‘I struggled to understand the main concepts of a particular paper, spending almost a year reading and rereading it. However, once I finally grasped its content, I found that I could quickly comprehend subsequent papers much more easily. Although the journey was challenging, it provided valuable insight into the research process as a scientist. Moreover, I am thrilled to be actively involved in this era and to contribute to the advancement of quantum computing. For decades, the development of quantum computers has largely remained theoretical. However, we now find ourselves in an era where researchers have access to real quantum devices. This transition marks a significant shift, as ideas, once confined to theoretical realms, are now being tested and validated through experiments. I find immense satisfaction in witnessing progress on both the theoretical and experimental fronts simultaneously.’

Zahra Mousavi | UoA

Zahra is a Ph.D. student with the Center for Research on Engineering Software Technologies (CREST) at the University of Adelaide. Her research interests include Natural language Processing and Information Extraction using Deep Learning methods and their applications in the Cyber Security domain. She has already worked on constructing a Persian wordnet and improving machine translation using this wordnet and verb sense disambiguation. Currently, she is working on security automation and orchestration leveraging NLP techniques.
Publications: Orcid

Tags: Deep Learning | Natural Language Processing | Cybersecurity

Zehang Deng | Swinburne University of Technology

Zehang Deng is a PhD student at Swinburne University of Technology, Melbourne. His current research focuses on machine learning, particularly the security and robustness of machine learning systems on different cloud devices. He is dedicated to exploring methods that ensure tradeoffs between model utility, privacy and efficiency in federated learning settings.

Zewei Shi | University of Melbourne

Zewei graduated from the University of Sydney with a Bachelor of Advanced Computing (Honours) degree in 2023. Before he started his PhD studies at the University of Melbourne, he worked as an intern research student at CSIRO’s Data61, focusing on AI security and privacy. Now, his research interests are detailed in Multimodal Large Language Model Security and Privacy, and he acts as a bridge to promote collaboration between the University of Melbourne and CSIRO’s Data61.
Tags: Trustworthy AI, Multimodal Large Language Model, Deceptive Pattern, Artificial Intelligence
Publications: Google Scholar

Zhibo Xu | Monash University

Zhibo Xu is a PhD student at Monash University since 2022 under the supervision of Dr Xingliang Yuan and Dr Shangqi Lai. Prior to that, he received the Bachelor of Computer and Network Engineering (H1) from RMIT University in 2020. His research interests include using cryptographic tools to protect machine learning models, which leads to safe and secure training and inference of machine learning and AI models.

Tags: Machine Learning Privacy and Security | Privacy-preserving Machine Learning

Zihan (Andrew) Wang | University of Queensland

Zihan (Andrew) Wang is a Ph.D. student at the School of Electrical Engineering and Computer Science, the University of Queensland. He is an innovative & adaptable student, with great problem-solving, communication, and teamwork skills. His current research focuses on tackling real-world security & privacy issues of machine learning systems in a formally verifiable manner. https://www.zihan.com.au/

Tags: Machine Learning | Privacy and Security | Formal Methods

Zihan (Luna) Ni | University of New South Wales

Luna is a PhD student of SEIT at the University of New South Wales Canberra. She received her MS degree from University of Twente and Eötvös Loránd University in 2020, and the BE degree from Nanjing University of Posts and Telecommunications in 2016. Her research focuses on the automated vulnerability detection and repair in software

Tags: Software security | IoT security | Mobile security