Find Academic Supervisors

• Cybersecurity and Privacy
• Data Management and Data Science
• Distributed Computing and Systems Software

Professor Jun Zhang is a leading academic in cybersecurity at Swinburne University, Australia, where he directs the Cybersecurity Lab. He has been honored as Australia's top cybersecurity researcher and instrumental in establishing Swinburne as a globally recognized cybersecurity research institution. His work includes high-impact papers and multi-million-dollar R&D projects, culminating in awards like the 2021 'Top Cybersecurity Research Institution' accolade. As course director of the Bachelor of Cyber Security, he pioneered an industry-driven teaching model with Deloitte and CSIRO, significantly boosting course enrollment. His collaborations extend to Adobe's Curriculum Innovation Program and the Australian P-TECH initiative, promoting STEM education and cybersecurity awareness. He supervises doctoral candidates and leads grants focused on AI-driven cybersecurity, smart home security, and blockchain-based edge computing. His research spans vulnerability detection, GAN forensics, IoT security, and privacy preservation in OSNs. Research interests include cybersecurity fundamentals, data science applications, and distributed systems. Notable achievements include the PTFix framework for Java vulnerabilities, the IoTFuzz smart home testing system, and CTI mining methodologies. Awards reflect his mid-career research excellence and industry partnerships. His grants with CSIRO and defense organizations emphasize real-world impact, addressing challenges from malware detection to adversarial machine learning. The Cybersecurity Lab and collaborative projects like Artchain demonstrate his commitment to bridging academia and industry. Professional activities include supervising over 20 HDR students and securing grants totaling millions. His work on blockchain-based edge storage (CSEdge) and SDCCP congestion control highlights innovation in networking. Future directions include advancing AI for design collaboration with CSIRO and enhancing privacy in smart energy technologies. His contributions span technical, educational, and community outreach domains, positioning him as a pivotal figure in cybersecurity's evolution.

Jon McCormackView profile

Professor

• Computational Creativity
• Human-machine interfaces
• Artificial Intelligence/Cybernetics
• +9 more

Jon McCormack is a Professor jointly appointed in Monash University's Faculty of Art, Design & Architecture (MADA) and Faculty of Information Technology. He founded and directs SensiLab, a research facility focusing on computational creativity, human-machine interfaces, and generative systems. His work spans electronic media art, evolutionary music, and artificial life. McCormack holds a PhD in Computer Science from Monash University, along with degrees in Computer Science, Applied Mathematics, and Film/Television. Research interests include computational creativity, tangible interfaces, and cybernetic systems. Notable projects include 'Explainable Artificial Creativity' (ARC-funded) and 'Building 4.0 CRC,' addressing architectural innovation through AI. He has been recognized with awards for collaborative projects like the Blundstone Intelligent Footwear for Healthcare. McCormack's recent articles explore AI-driven art, generative systems, and interdisciplinary design. His work bridges artistic practice with technical innovation, emphasizing ethical and creative dimensions of human-AI collaboration. SensiLab serves as a hub for practice-based research in digital media and interactive systems. Education: PhD in Computer Science, Monash University (2004) Bachelor of Science (Honours), Computer Science/Applied Mathematics, Monash University (1987) Graduate Diploma in Film/TV, Swinburne University (1986) Bachelor of Science, Computer Science/Applied Mathematics, Monash University (1985) Key Projects: Lead investigator on 'Explainable Artificial Creativity' (2022–2026) Co-investigator in 'Building 4.0 CRC' (2020–2027), exploring AI-driven architectural design Awards: 2022 Designers Australia Award for Blundstone Footwear 2020 'On the Machine Condition' Prize McCormack's lab, SensiLab, fosters collaborations across disciplines, producing exhibitions, software, and theoretical frameworks for computational creativity. He actively supervises PhD students in practice-based research, emphasizing the intersection of art and technology.

Aldeida AletiView profile

Professor

• Automated Software Engineering
• Artificial Intelligence
• Optimization
• +2 more

Aldeida Aleti is a Professor in the Department of Software Systems & Cybersecurity at Monash University. Her research focuses on Automated Software Engineering, leveraging AI and optimization techniques for tasks like software design, testing, and repair. She has held roles including Chief Examiner for units like FIT4002 and FIT5136, and has contributed to teaching FIT3077 and FIT1008. Education: PhD in Software Engineering (Swinburne University of Technology, 2012), Master of Computer Engineering (Polytechnic University of Tirana, 2008), and Bachelor Honours in Computer Engineering (Yildiz Technical University, 2005). Research Interests: Automated software engineering, fitness landscape analysis, optimization, and search-based techniques. She leads projects like RAISE (Responsible AI Software Engineering) and collaborates on quantum computing and healthcare AI initiatives. Awards include the FIT Dean's Award (2016), Best Paper Awards (2015, 2011), and the Heidelberg Laureate Forum invitation (2014). She has been a grant assessor for the Australian Research Council since 2015. Advising: Accepting PhD students in AI-driven software engineering, optimization, and automated testing. Active in committees like the Faculty Research Committee and Early Career Researcher committee.

Natasha FernandesView profile

Senior Lecturer

• Data Privacy
• Differential Privacy
• Privacy-Preserving Machine Learning
• +3 more

Natasha Fernandes is a Senior Lecturer in the School of Computing at Macquarie University. She holds a PhD in Computing from Macquarie University and École Polytechnique (France), and an undergraduate degree in Pure Mathematics and Computer Science from the University of Sydney. Her roles include involvement in the Data Horizons Research Centre and Future Communications Research Centre, alongside professional casual appointments in academic computing. Her research focuses on the mathematical foundations of data privacy, particularly differential privacy and its applications in natural language processing and machine learning. She develops privacy-preserving systems and tools using quantitative information flow techniques rooted in information theory. Key areas include privacy analyses for financial systems (e.g., Open Banking), API privacy, and optimizing utility in privacy pipelines. Fernandes has led or contributed to five research projects, including work on privacy analyses for financial transaction protocols and UAV-based machine learning systems. She received the 2021 John Makpeace Bennett Award for her doctoral research on differential privacy in metric spaces. Her academic journey combines industry experience as a backend software engineer with rigorous academic contributions, spanning over 21 peer-reviewed publications and collaborations across cybersecurity and privacy engineering domains.

Sandaru SeneviratneView profile

Research Fellow

• Natural Language Processing
• Machine Learning
• Text Simplification and Generation
• +1 more

Sandaru Seneviratne is a Research Fellow at the School of Computing , The Australian National University , focusing on Natural Language Processing (NLP), Machine Learning, Text Simplification, and Health Informatics. His work bridges advanced language technologies with healthcare applications. PhD in Computer Science, The Australian National University Bachelor of Computer Science and Engineering, University of Moratuwa Research Interests His NLP research emphasizes text simplification frameworks like TextSimplifier and Prompt-based methods for multilingual contexts (e.g., English-Sinhala translation). He investigates factuality error detection in simplified text and lexical substitution techniques for accessibility. In Health Informatics, he contributes to medical term identification using neural networks (CNNs/Transformers), CLEF eHealth evaluations, and diabetes management technologies for teens. Earlier work includes clustering word embeddings for knowledge graphs and restaurant domain information extraction. Publication Trends His publications span 2018–2024, with recent focus on multilingual text simplification, factuality in AI-generated text, and healthcare information retrieval. Technical methods include transformers, triplet networks, and hierarchical clustering, often applied to medical/health domains.

• Human Computer Interaction
• User Experience
• Information systems
• +8 more

Assoc Prof Geraldine Torrisi is the Deputy Head of School (Learning & Teaching) at the School of Information and Communication Technology at Griffith University. With a PhD in Educational Studies and degrees in Science (Physics/Mathematics) and Education, she focuses on Human-Computer Interaction (HCI) , User Experience , and Learning Theory . She is a Senior Fellow of the Higher Education Academy and Griffith Learning and Teaching Academy . Education: PhD in Educational Studies (Griffith University) Masters of Educational Studies (James Cook University) Bachelor of Education (Secondary Science and Maths) (James Cook University) Bachelor of Science (Physics/Mathematics) (James Cook University) Research Interests include Interactive Digital Media , Educational Technology , and Data Literacy . Her 2025 book chapters explore AI's role in education, while 2024-2023 articles address data literacy frameworks and hybrid teaching models. Scientific Awards include Griffith Award for Excellence in Teaching (2011) , Excellence in Teaching Award (2015) , and Senior Fellow of the Higher Education Academy (2018) . She also received multiple Group Learning and Teaching Commendations (2009, 2017, 2019). Grants include The Dignity Project (2024-2025), Promoting Renewable Energy Adoption (2022-2025), and Smart Environmental Sensing (2022-2023). She supervises PhD students on topics like Human-Machine Teaming and Instructional AR Design .

• Data Management
• Social Media Analysis
• User Behavior Analysis
• +4 more

Dr. Ying Zhou is a Lecturer in the School of Computer Science at The University of Sydney. She holds a BSc and MEng from Nanjing University (1997) and a PhD from the School of Computing at the National University of Singapore (2003). Her research focuses on human-centred data management, including large-scale data storage, user behavior analysis, and improving image query systems. Current projects include mining socially tagged images and accountability mechanisms for multitenant cloud platforms. Teaching responsibilities include courses on Advanced Data Models (COMP5338), e-Commerce Technology (COMP5347), and Cloud Computing (COMP5349). She collaborates with industry partners like Amazon and IBM, and advises four research students. She is a member of the Sydney Southeast Asia Centre. Her publication record spans 20 years, with recent work emphasizing machine learning applications, big data systems, and cloud security. Research trends include optimizing distributed computing frameworks (Hadoop/Spark/Flink), adversarial machine learning, and trustworthy database systems. Earlier work focused on social network analysis, web communities, and blogosphere dynamics. Current research projects address both technical challenges (e.g., efficient image search, cloud platform accountability) and applied solutions for leveraging social media data. Her lab activities involve interdisciplinary collaboration between computer science and information systems domains.

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

• Robotics
• Autonomous Systems
• Data Fusion
• +7 more

Hugh Durrant-Whyte is a Professor at the University of Sydney and Director of the Centre for Translational Data Science. He holds a BSc (Eng) from the University of London, an MSE, and a PhD from the University of Pennsylvania. Previously, he served as CEO of National ICT Australia (NICTA) and Director of the Australian Centre for Field Robotics (ACFR). His research focuses on robotics, autonomous systems, and data fusion, with over 350 publications and four successful startups. Education: BSc (Eng) in Engineering, University of London MSE in Robotics, University of Pennsylvania PhD in Robotics, University of Pennsylvania Affiliations: Director, Centre for Translational Data Science Former CEO, NICTA (2010-2014) Former Director, ACFR (1995-2010) His research interests span robotics, autonomous systems, and sensor networks. Notable contributions include foundational work on SLAM (Simultaneous Localization and Mapping) and decentralized data fusion. He has pioneered applications in mining automation, autonomous vehicles, and environmental modeling. His work emphasizes practical real-world systems, with projects like autonomous straddle carriers for container terminals and terrain mapping for mining operations. Awards & Honors: NSW Scientist of the Year (2010) Fellow of the Royal Society (FRS), Australian Academy of Science (FAA), and IEEE (FIEEE) Recipient of multiple IEEE Best Paper awards Grants & Labs: Leadership in securing multi-million-dollar grants for robotics and data science initiatives Centre for Translational Data Science: Focuses on translating data science into real-world impact He advises on numerous government and industry projects, bridging academic research with industrial applications. His research teams have developed influential algorithms for autonomous navigation and multi-agent systems.

• Drone Autonomy
• Drone-Based Remote Sensing
• Aerospace Engineering
• +7 more

Dr. Fernando Vanegas Alvarez is a Lecturer at the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT). He holds a M.Sc. in Electrical Engineering from Halmstad University and a PhD in Aerial Robotics from QUT. His research focuses on Drone Autonomy, UAV navigation in GNSS-denied environments, and AI-assisted remote sensing. Education: M.Sc. in Electrical Engineering, Halmstad University PhD in Aerial Robotics, QUT Research Interests: Dr. Vanegas' work emphasizes motion planning for UAV exploration , POMDP , SLAM , Visual Odometry , and AI-driven remote sensing applications . His projects include UAV frameworks for planetary exploration, invasive species mapping, and multi-UAV coordination in challenging environments. Scientific Awards: Advanced Queensland Industry Research Fellowship Grant (2024) Supervision & Grants: Dr. Vanegas has supervised three postgraduate students, including topics like planetary exploration UAV systems and multi-agent UAV search algorithms. He actively accepts new students for Honours, Masters, and PhD programs. His research is supported by grants and collaborations with the QUT Centre for Robotics. Teams & Affiliations: He is a core member of the QUT Centre for Robotics and contributes to the School of Electrical Engineering & Robotics. His work bridges robotics, AI, and environmental science.

Veronika KuchtaView profile

Assistant Professor

• Post-Quantum Cryptography
• Mathematical Cryptography
• Blockchain Security
• +3 more

Dr. Veronika Kuchta is an Assistant Professor at Florida Atlantic University's Department of Mathematical Sciences, specializing in post-quantum cryptography and its applications to blockchain. She holds a PhD in applied cryptography from the University of Surrey (2016) and has held roles including Honorary Research Fellow at The University of Queensland (UQ) School of Mathematics and Physics (2020–2022), Research Fellow at Monash University (2018–2020), and postdoc at Université libre de Bruxelles (2016–2018). Education: PhD in Applied Cryptography, University of Surrey, UK (2016) Diploma in Mathematics, Heidelberg University, Germany (2010) Research Interests: Her work focuses on constructing quantum-resistant cryptographic primitives, including lattice-based schemes, zero-knowledge proofs, and their integration into real-world systems like blockchain. She explores topics such as post-quantum verifiable random functions, cryptographic accelerators, and privacy-preserving authentication protocols. Key Projects: Formal Verification of Post-Quantum Cryptographic Primitives (UQ Cyber Seed Funding, 2021–2023) Algorand Centre of Excellence on Sustainability Informatics (2022–2023) Grants & Funding: Noted for contributions to blockchain security and lattice-based protocols, her work is supported by industry and institutional grants, including the Monash University Open Grant for the Algorand Centre of Excellence. Labs/Teams: Collaborates with interdisciplinary teams on cryptographic protocol design and hardware-accelerated cryptography (e.g., TPU-based cryptographic acceleration).

• Algorithm development
• Data analytics
• Time-series data analysis
• +6 more

Emran Ali is a Graduate Researcher (Ph.D. candidate) and Part-Time Lecturer at Deakin University's School of Information Technology within the Faculty of Science, Engineering and Built Environment. He holds concurrent faculty appointments at Hajee Mohammad Danesh Science & Technology University (HSTU) in Bangladesh where he teaches computer science courses while on study leave. His academic journey includes a Master of Science (Research) in Information Technology from Deakin University (2022) and a Bachelor of Science in Computer Science and Engineering from HSTU. Doctor of Philosophy (Ph.D.) in Information Technology, Deakin University (2023–present) Doctor of Philosophy (Ph.D.) in Machine Learning, Coventry University (Cotutelle program, 2023–present) Master of Science (Research) in Information Technology, Deakin University (2020–2022) Bachelor of Science in Computer Science and Engineering, HSTU Bangladesh (2007–2012) Ali's research focuses on algorithm development and applied machine learning in health informatics, specializing in biosignal processing for neurological and sleep disorder detection. His work integrates time-series data analysis with explainable AI techniques to develop clinical decision support systems. Current projects include ML/DL modeling of sleep-stage transitions in aging populations and causal relationship analysis in sleep disorders using EEG data. Analysis of his 10 recent publications reveals strong concentration in biomedical ML applications (60%), particularly EEG-based neurological disorder detection and mental health diagnostics. Secondary focus areas include environmental monitoring systems (20%) and foundational computer science (20%). His work consistently employs ensemble methods and feature optimization techniques across diverse datasets, with increasing emphasis on real-world clinical applicability in recent publications. Deakin University Post-graduate Research Scholarship (DUPRS) through Cotutelle program with Coventry University National Fellowship from Bangladesh Ministry of Science and Technology (2020) Best Presentation Award at Deakin School of IT Conference (2021) AWS AI/ML Scholarships (2023, 2024) Next Generation Tech Booster Scholarship (2024) Ali provides research supervision at HSTU while serving as a Graduate Research Teaching Fellow at Deakin University for Machine Learning and Data Analytics units. His industry collaborations include projects with Monash University, Alfred Health, and AETMOS Australia focused on health informatics applications. Current funding includes AWS-sponsored nanodegrees and Deakin University research scholarships supporting his sleep disorder research. His technical work integrates cloud-based AI/ML platforms (AWS, Azure) with biosignal processing pipelines, utilizing collaborations across Australian healthcare institutions to validate clinical applications. Recent projects emphasize explainability in deep learning models for medical diagnostics, particularly in resource-constrained environments relevant to Bangladesh healthcare contexts.

• Empirical Software Engineering
• Requirements Engineering
• Applied Natural Language Processing
• +7 more

Dr. Chetan Arora is a Senior Lecturer in Software Engineering at Deakin University's School of Information Technology, part of the Faculty of Science Engineering and Built Environment. He holds a PhD from the University of Luxembourg where he received the best PhD thesis award in the ICT domain. His research focuses on applied Artificial Intelligence in Software Engineering, with particular emphasis on Empirical Software Engineering, Requirements Engineering, and Applied Natural Language Processing. PhD in Computer Science from University of Luxembourg Masters in Software Engineering from Technische Universitat Kaiserslautern (Germany) Bachelors in Engineering (CS) from Thapar University (India) Arora's research interests center on the intersection of AI and Software Engineering, particularly how machine learning and natural language processing can enhance software development processes. His work explores requirements engineering, test automation, software trustworthiness, and human-centric software development. He investigates how large language models can be effectively deployed for tasks like test case generation, requirements analysis, and traceability. His recent publications reveal a strong focus on practical applications of AI in software engineering, with numerous studies examining the real-world implementation challenges and benefits. His publication record shows significant activity in top software engineering venues, with a notable emphasis on AI applications in software engineering processes. His recent work demonstrates expertise in retrieval-augmented generation systems, requirements-driven testing, and human-centric software development approaches. The publications collectively highlight his focus on bridging theoretical AI advancements with practical software engineering challenges. Best Ph.D. thesis award in the ICT domain at University of Luxembourg Arora actively supervises doctoral students working on cutting-edge topics including satellite communication systems, extended reality applications, and human-centered AI requirements engineering. His industry collaborations include work with Department of Defence on projects like Contextually Situated Anomaly Detection and Planning and Optimisation of Resources in Defence Satellite Communication Systems. He previously worked at SES Satellites on applied AI for IoT and Satcom, and as an FNR-PPP research fellow at the University of Luxembourg in software quality assurance. His laboratory work focuses on developing practical AI solutions for software engineering challenges, particularly in requirements engineering and test automation. Current projects involve multi-orbit satellite constellation optimization, dynamic radio resource management, and extended reality enabled human-centric requirements engineering.

Peter RobinsonView profile

Research Fellow

• Computer Science
• Software Verification
• Formal Methods
• +6 more

Dr. Peter J. Robinson is an Honorary Research Fellow at the School of Electrical Engineering & Computer Science, The University of Queensland. His academic career spans over three decades, focusing on foundational research in programming languages, formal methods, and distributed systems. His work includes contributions to Qu-Prolog, a multi-threaded Prolog implementation, and TeleoR, a robotic task programming framework. Research interests include agent-based systems, blockchain security for aerospace applications, software verification, concurrent programming, and education technology. Notable projects include the Pedro publish/subscribe server and MyPyTutor, an interactive Python learning tool. He has collaborated on railway safety protocols and spacecraft control systems using blockchain. Publications span journals like Formal Aspects of Computing and conferences such as IEEE Symposium on Computers and Communications. Technical reports include work on unification algorithms and multi-agent verification frameworks. He has advised on projects involving IoT architectures and swarm intelligence simulations.

Khandakar AhmedView profile

Associate Professor

• Artificial Intelligence
• Cyber Security
• Digital Health
• +8 more

Khandakar Ahmed is an Associate Professor of Information Technology at Victoria University's College of Arts, Business, Law, Education & IT (CoABLEIT) and Discipline Leader for Emerging Trends in Science, IT & Engineering at the Institute for Sustainable Industries and Liveable Cities (ISILC). His career spans roles at Victoria University (2017–present), RMIT University (2015–2017), and Shahjalal University of Science and Technology (2007–2015). Education: PhD in Electrical and Computer Engineering (RMIT University, 2014), MSc in Networking and e-Business Centered Computing (University of Reading, 2009), BSc in Computer Science and Engineering (Shahjalal University of Science and Technology, 2007) His research expertise includes Artificial Intelligence, Cyber Security, Digital Health, Federated Learning, Quantum Computing, and the Internet of Things . He has published over 100 refereed papers with 4,200+ citations (h-index 32) and secured $3.4 million+ in funding since 2018 from partners like Australia's Economic Accelerator and Western Health. His recent work focuses on AI-driven mental health analysis , blockchain security frameworks , and edge computing optimizations . He serves as lead supervisor for 6 PhD and 1 Master of Research student and has mentored over 100 students through industry-linked projects. Dr Ahmed actively contributes to editorial roles for leading journals such as Scientific Reports , IEEE Transactions , and ACM Computing Surveys . His research aligns with UN Sustainable Development Goals 3 (Good Health), 9 (Industry Innovation), and 11 (Sustainable Cities).