Find Academic Supervisors

• Geostatistics
• Big Data Processing
• Computer Systems
• +6 more

Dr. John Zigman is an academic affiliated with the Faculty of Engineering at The University of Sydney. His research spans interdisciplinary fields including geostatistics, big data processing, and computer systems optimization. He has contributed to advancements in geological boundary modeling, memory management frameworks for hybrid systems, and Java performance analysis. Key research interests include: Geological modeling using Bayesian approaches and dynamic time warping Development of holistic memory management systems (e.g., Panthera framework) Optimization of parallel computing architectures and distributed garbage collection Integration of geophysical data with autonomous spatial modeling algorithms Recent work focuses on photonic integrated circuits (2025), Java performance re-engineering (2025), and large-scale geological boundary rectification (2023). His publications demonstrate expertise in both theoretical computer science and applied geosciences. Notable collaborations include work with Prof. Arman Melkumyan on geostatistical applications and Prof. Stephen Blackburn on persistent Java systems. His research bridges computational methods with earth sciences, addressing challenges in resource exploration and data-intensive systems.

• Parallel and Distributed Computing
• Networking
• Complex Systems
• +5 more

Albert Zomaya is the Peter Nicol Russell Chair Professor of Computer Science and Director of the Centre for Distributed and High-Performance Computing at The University of Sydney. He holds a PhD and is a Fellow of multiple prestigious institutions including the Australian Academy of Science and the Royal Society of New South Wales. His research focuses on parallel and distributed computing, networking, and complex systems, with applications in cybersecurity, cloud computing, and IoT. He has authored over 700 publications, 30 books, and edited numerous journals. His awards include the ACM MSWIM Reginald A. Fessenden Award and the New South Wales Premier’s Prize for Engineering. Education: PhD, along with extensive academic and industry experience across multiple institutions including the University of Western Australia and the University of Sydney. Research interests span distributed computing, high-performance systems, energy-efficient scheduling, and bioinformatics applications. His work impacts national security, healthcare, and industrial productivity through advancements in computing technologies. Awards highlight his contributions to scalable computing, parallel processing, and sustainable computing. He serves on editorial boards of major journals like ACM Computing Surveys and IEEE Transactions on Computers. Advising and grants include mentoring numerous PhD students and leading projects like the ARC Professorial Fellowship on energy-aware scheduling. He directs research centers and collaborates with industry partners to translate theoretical advancements into practical solutions. Labs and teams: Leads the Centre for Distributed and High Performance Computing, and collaborates with institutions like the Net Zero Institute and the University of Sydney Nano Institute.

Peter BohmView profile

Research Fellow

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Dr. Peter Bohm is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advanced robotics, reinforcement learning, and real-world implementation of intelligent systems. His work explores topics such as legged manipulators, low-cost robotic systems, and parallelized frameworks for multi-agent coordination. Recent contributions include studies on dynamic throwing motions, feature extraction for deep reinforcement learning, and asynchronous training methods. Bohm’s research emphasizes practical applications of AI in robotics, with a particular interest in control systems and embedded solutions. His publications span robotics, machine learning, and distributed computing domains, reflecting a multidisciplinary approach to solving complex technical challenges. No academic awards or grants are explicitly listed in the provided materials. He currently holds no recorded advisees.

Antony HoskingView profile

Professor

• Programming Languages
• Software Systems
• Software Assurance
• +7 more

Antony Hosking is a Professor and School Director of the School of Computing at the Australian National University (ANU). He holds a BSc in Mathematical Sciences from the University of Adelaide (1985), MSc with First Class Honours in Computer Science from the University of Waikato (1987), and a PhD in Computer Science from the University of Massachusetts Amherst (1995). Prior to ANU, he served for over 20 years at Purdue University as Assistant Professor and later tenured Associate Professor of Computer Science. He is also an Honorary Professor at the University of Massachusetts Amherst. His research focuses on programming languages, software systems, and software assurance, with particular expertise in garbage collection, memory management, compiler optimizations, and architectural support for programming languages. He co-authored the seminal reference text *The Garbage Collection Handbook*, now in its second edition (2023), and has contributed extensively to conferences like ACM SIGPLAN. He has held leadership roles in professional organizations, including the Executive Committee of ACM SIGPLAN and the International Symposium on Memory Management. Hosking’s accolades include being a Distinguished Scientist of the Association for Computing Machinery (ACM), an elected member of Association Internationale pour les Technologies Objets (AITO), and a Professional Member of IEEE. His consulting work involves intellectual property litigation in US courts and the US Patent and Trademark Office. He has advised on high-performance garbage collectors, concurrency, and real-time systems, with a focus on energy-aware and persistent storage solutions. His recent work addresses foundational assurance in systems software, aiming to build trustworthy systems. He collaborates on projects like the Single-Chip Cloud Computer and explores portable performance for parallel managed languages across multi-core architectures.

• Cold Neutral Medium
• Small Magellanic Cloud
• Milky Way
• +3 more

Dr. Antoine Marchal is a Researcher at the Research School of Astronomy & Astrophysics, Australian National University, specializing in observational studies of the interstellar medium with emphasis on cold neutral hydrogen structures. His core research domains include: Cold Neutral Medium Small Magellanic Cloud Milky Way Magellanic Clouds Interstellar Matter He employs advanced radio astronomy techniques including absorption spectroscopy and Fourier analysis of HI 21 cm emission to investigate gas phase transitions and galactic structure. From 2022-2025, Dr. Marchal published 9 articles in leading journals like Monthly Notices of the Royal Astronomical Society and Astronomy and Astrophysics , demonstrating consistent focus on cold gas diagnostics through stacking methods and spectral analysis. His work frequently leverages data from ASKAP and GASKAP surveys, revealing multi-phase cloud structures in Magellanic systems and Milky Way cirrus regions. Registered to supervise ANU research students, Dr. Marchal contributes to major international collaborations including GASKAP and DHIGLS. His methodological innovations in supercomputing-based data processing highlight technical expertise alongside astrophysical insights. He actively participates in the GASKAP survey team and DHIGLS collaboration, working with global partners to map neutral hydrogen distributions across galactic environments using next-generation radio telescopes.

• Computer Science
• Information Technology
• Marine Conservation Technology
• +3 more

Jason Holdsworth is a Lecturer at James Cook University (JCU), specializing in Digital Technology and Computer Science. He holds a PhD in Computer Science from JCU, with earlier degrees including a BSc (Hons) and a double major BSc in Computer Science from the same institution. His research focuses on applying computer technology to real-world challenges, such as marine conservation through mobile apps and image classification systems for WWF, and improving abalone farming via image segmentation techniques. He is also engaged in educational technology, particularly enhancing adult learner engagement through digital methods. Holdsworth has been recognized with awards including the Advance Queensland Community Digital Champion (2017) and CSIRO STEM Professional (2017). He actively contributes to JCU’s Indigenous Australian Datathon Steering Committee and has advised students on topics like autonomous CNN architecture selection. His work spans healthcare technology (e.g., VR in hemodialysis), embedded systems (C++20 coroutines on microcontrollers), and collaborative learning tools (e.g., academagogy frameworks). Over 41 publications highlight his interdisciplinary research, including studies on asynchronous IoT programming, mobile application performance models, and collaborative emergency management systems. His work often bridges theory and practice, emphasizing sustainable tech solutions for education, healthcare, and environmental conservation.

• Deep Learning
• Machine Learning for Biological and Medical Data
• Artificial Intelligence
• +4 more

Dr Benjamin Mashford is a Research Fellow at the John Curtin School of Medical Research, Australian National University (ANU), specializing in interdisciplinary applications of deep learning, machine learning, and nanoelectronics to biomedical challenges. His work bridges computational methods with immunology, infectious diseases, and advanced diagnostics. Affiliation: Division of Immunology and Infectious Diseases, ANU Research Group: The Enders Group - Models of Human Primary Immunodeficiencies Research Focus: Mashford's work spans: Deep learning for biological data analysis (flow cytometry, EEG, genomic sequences) Neuromorphic and low-power computing systems Quantum dot technology for optoelectronics Energy-efficient health-monitoring devices Medical signal processing and predictive analytics Publication Trends: Recent work emphasizes hyper-dimensional cytometry representations, automated phenotyping systems, and cross-domain applications of machine learning from immunology to mineral processing. He also explores hardware implementations for biomedical diagnostics, including neurostimulation and fall detection systems. Supervision: Registered as a supervisor for graduate research students.

Zifan LinView profile

Lecturer

• Image Enhancement
• Power Electronics
• Inverter Technology
• +3 more

Zifan Lin is a Lecturer and PhD student in the Multi-Discipline Doctor of Philosophy program at the School of Engineering, University of Western Australia. His research focuses on power electronics, underwater imaging, and control systems, with notable contributions to inverter technology, DC microgrid systems, and machine learning applications in vision systems. He actively collaborates on projects spanning Australia and other regions, addressing challenges in renewable energy integration and advanced image processing. His academic background includes multi-disciplinary training in engineering, complementing his teaching duties. Research interests emphasize practical solutions for energy systems (e.g., DC-DC converters, NPC inverters) and underwater image enhancement networks like TANet and ReX-Net. Recent work explores semantic feature refinement for mask detection and robust control schemes for electric motors using reinforcement learning. Publications highlight innovations in space vector modulation strategies, finite-set model predictive control, and agricultural drying technologies. While no specific awards are listed, his prolific output in top journals like IEEE Transactions and LWT-FOOD SCIENCE AND TECHNOLOGY reflects academic recognition. His advising and grant activities remain unspecified in available data.

• Optimization
• Signal Processing
• Machine Learning
• +3 more

Dr. Minh Dao is a Senior Lecturer in the Department of Mathematics at RMIT University's School of Science. His research focuses on optimization theory, algorithm design, and their applications in signal processing, machine learning, and wireless communications. He has contributed to advancements in distributed optimization, nonconvex programming, and federated learning frameworks. Key research interests include operator splitting methods, DC programming, and mathematical modeling for energy-efficient systems. He has published extensively in top-tier journals and conferences such as the European Journal of Operational Research and IEEE Transactions on Intelligent Transportation Systems. Dr. Dao supervises research projects on distributed optimization for federated learning, reboot sensing MIMO systems, and DC programming approaches for clustering problems. His work integrates theoretical analysis with practical applications in telecommunications and energy networks.

• Cryptography
• Machine Learning
• Database Systems
• +3 more

Michael Brand is an Adjunct Professor at RMIT University's School of Computing Technologies. His research focuses on cryptography, privacy-preserving technologies, machine learning, and algorithm design. Key areas include zero-knowledge proofs, secure financial systems like FinTracer, and computational theory. His work bridges theoretical foundations with practical applications in data security and real-time processing systems. Research interests span advanced cryptographic protocols, privacy-enhancing machine learning techniques, and distributed computing paradigms. Notable contributions include innovations in blockchain tracing mechanisms and foundational work in statistical estimation theory. He actively publishes in top-tier venues, addressing challenges in secure computation, network analysis, and database systems. Collaborations emphasize interdisciplinary solutions to modern computational and security challenges.

• Pipeline Engineering
• Computational Geomechanics
• Geotechnical Earthquake Engineering
• +5 more

Dr. George (Georgios) Kouretzis is an Associate Professor in the School of Engineering at the University of Newcastle. His research focuses on pipeline engineering, soil-structure interaction, computational geomechanics, and geotechnical earthquake engineering. He has contributed to the development of practical design tools for buried pipelines affected by geohazards and seismic events. His work integrates advanced numerical simulations with experimental methods, such as physical modeling and vane shear testing. Educations: PhD in Geotechnical Engineering, National Technical University of Athens (NTUA), Greece (2005) MSc in Civil Engineering, NTUA, Greece Diploma in Civil Engineering, Democritus University of Thrace, Greece Research Interests: Onshore pipeline infrastructure safety under environmental and seismic hazards Soil dynamics and computational geomechanics Seismic design of tunnels and geotechnical structures Advanced constitutive models for soft soils Dr. Kouretzis has led major projects funded by the Australian Research Council (ARC), focusing on environmental impacts on pipelines and laboratory apparatus development. He collaborates internationally, aiming to establish a national buried pipeline research hub. His awards include recognition for research and teaching excellence from IACMAG and ACCM. He serves as an associate editor for the Canadian Geotechnical Journal and actively reviews for leading journals and funding bodies. His academic roles include course coordination for geotechnical engineering courses and supervision of postgraduate students. Parallel to academia, he provides expert consultancy for energy and transportation infrastructure projects, including pipelines and seismic-resistant tunnels. He is a member of the Australian Geomechanics Society and served as Secretary of its NSW-Newcastle Chapter.

• Operating systems
• Distributed systems
• Computer Science
• +3 more

Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +5 more

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.

• Digital Rock Physics
• Computational Pore-Scale Physics
• Tomographic Imaging
• +9 more

Professor Christoph Arns is a full Professor at the University of New South Wales (UNSW) School of Engineering, specializing in Mineral and Energy Resources Engineering. He has been at UNSW since 2008 and has held the position of full Professor since 2014. Currently, he leads the Geoenergy and Geostorage discipline within the school. Prior to joining UNSW, he was a research fellow at the Australian National University from 2001-2008. Professor Arns obtained his Dipl. Phys. from RWTH Aachen, Germany in 1996 and his PhD in Petroleum Engineering from UNSW Sydney in 2002. His academic background combines physics and petroleum engineering, creating a unique interdisciplinary perspective for his research. His primary research focus is on Digital Rock Physics, where he has pioneered computational pore-scale physics based on tomographic images. He specializes in integrating 3D tomographic imaging technology with NMR techniques for petrophysical applications, with particular emphasis on heterogeneity analysis. His work spans multiple sub-disciplines including pore-scale modeling, digital core analysis, rock physics, reservoir characterization, and Minkowski functionals for structural analysis. Professor Arns has developed the MPI-parallel software package 'morphy' for computational physics operating on segmented tomographic images, which includes sophisticated NMR response modeling, electrical property calculations, permeability estimation, elastic moduli determination, and morphological property analysis. His research has substantial industry relevance, as evidenced by his role as a founding member of Digital Core Pty Ltd, an ANU/UNSW spin-off that was sold to FEI for $76 million in 2014 and is now part of ThermoFisher. He has received significant research support through three successive Australian Research Council (ARC) fellowships. His scholarly contributions are reflected in numerous publications spanning from 2000 to 2025, with recent work focusing on advanced computational methods, machine learning applications in rock physics, and detailed analysis of fluid-rock interactions at the pore scale. Professor Arns maintains active leadership roles in multiple professional societies including Interpore (lifetime member, former council chair 2016-2019), Society of Core Analysts (Australasia Regional Director since 2016), Society of Petrophysicists & Well Log Analysts, Society of Exploration Geophysicists, and Society of Petroleum Engineers.