Find Academic Supervisors

Dr. Sara Ballouz is a Senior Lecturer at the University of New South Wales (UNSW) , affiliated with the Garvan Institute of Medical Research and the Garvan-Weizmann Centre for Cellular Genomics . Her research integrates computational and experimental approaches to understand the genetic architecture of disease , with a focus on X-linked disorders , sex differences in disease , and personalized medicine . 2013 - PhD, Victor Chang Cardiac Research Institute and UNSW 2008 - BE Bioinformatics (First Class Honours), UNSW 2008 - BSc Genetics, UNSW Her work addresses challenges in analyzing multi-omics data (genomes, transcriptomes, epigenomes, proteomes) to develop robust tools for personalized medicine. Key contributions include improving RNA-seq accuracy through pan-human consensus genomes and creating computational methods like AuPairWise and CoCoCoNet for co-expression analysis. She explores developmental stochasticity and its transcriptional legacy, alongside cross-tissue X-chromosome inactivation patterns. Recent publications emphasize single-cell transcriptomics applications in autoimmune diseases (e.g., X-linked chronic granulomatous disease ), long COVID immune dysregulation , and cardiometabolic disease treatment. Her group at Garvan-Weizmann Centre leverages computational genomics and meta-analysis to address reproducibility in biomedical research. Dr. Ballouz supervises students including Lachlan Gray , who investigates X chromosome roles in female autoimmune disease . She collaborates with institutions like Cold Spring Harbor Laboratory and Victor Chang Cardiac Research Institute . Email: s.ballouz@unsw.edu.au

• Transcriptomics (single-cell and bulk)
• Co-expression
• X-linked disorders
• +3 more

Dr. Kit Yan Chan is a Senior Lecturer at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University. His research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with applications in wireless communications, signal processing, and power systems. He has held editorial roles in journals such as Neurocomputing, Sensors, and the International Journal of Ad Hoc and Ubiquitous Computing. His teaching spans courses like Transmission and Interface Design, Advanced Research in AI, and Mobile Cloud Computing. Dr. Chan's work emphasizes interdisciplinary approaches, combining computational intelligence with engineering challenges. He has contributed to over 100 publications in areas such as resource allocation in heterogeneous networks, deep learning for load forecasting, and underwater acoustic communication systems. His research bridges theoretical advancements and practical implementations, addressing real-world problems in telecommunications, energy systems, and smart technologies. His recent projects include optimizing energy efficiency in 5G networks, developing robust power control strategies, and advancing neural network architectures for real-time applications. Collaborations with industry and global institutions underscore his commitment to impactful research.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +5 more

Dr. Marwa Keshk is a Lecturer in Cyber Security at the School of Professional Studies, University of New South Wales (UNSW) Canberra. Her academic career focuses on the intersection of cybersecurity and artificial intelligence, with particular expertise in privacy preservation, anomaly detection, and threat intelligence. Her educational background includes: PhD in Cyber Security and Privacy Preservation (2021) from UNSW Master's degree in Evolutionary Computation (2017) from UNSW Bachelor's degree in Computer Science (2012) from the Faculty of Computers and Information at Helwan University, Egypt Dr. Keshk's research interests primarily center around the application of artificial intelligence and machine learning techniques to enhance cybersecurity measures. Her work explores how computational intelligence, statistical methods, and emerging technologies can be leveraged for privacy preservation, anomaly detection, and threat intelligence. She has made significant contributions to the security of Internet of Things (IoT) networks, cyber-physical systems, and critical infrastructure protection. Her research often combines theoretical advancements with practical applications, addressing real-world security challenges in increasingly connected digital environments. Analysis of Dr. Keshk's publication history reveals a strong focus on applying AI and machine learning to cybersecurity challenges, particularly in IoT environments. Her work demonstrates a progression from foundational research in evolutionary computation to specialized applications in cybersecurity. Recent publications show increasing emphasis on explainable AI for security applications, reflecting the growing need for transparency in security decision-making processes. Her research spans multiple domains including smart cities, industrial control systems, and power networks, demonstrating the versatility and wide applicability of her security frameworks. Dr. Keshk has received notable recognition for her academic work: UNSW Tuition Fee Scholarship for PhD Research (2018-2021) Data61/CSIRO Scholarship (2019-2021) During her PhD studies, Dr. Keshk was a research candidate at Data61-CSIRO in Australia, providing her with valuable industry experience alongside her academic pursuits. Her collaborative approach to research is evident in her extensive co-authorship with colleagues across various institutions, particularly with researchers from UNSW and other Australian universities. Dr. Keshk's work contributes significantly to several research teams and initiatives focused on cybersecurity and AI at UNSW Canberra. Her expertise complements broader efforts in secure systems development, particularly in the context of increasingly connected environments where traditional security boundaries are challenged by new technologies.

• Cyber Security
• Artificial Intelligence
• Privacy Preservation
• +5 more

Professor Marcel Dinger is a prominent academic and researcher currently serving as Professor and Head of School for Biotechnology and Biomolecular Sciences at UNSW Sydney. With over 20 years of experience in genomics, he has established himself as a leading figure in both academic and entrepreneurial spheres within the field. He has published 153 papers with over 24,000 citations and maintains an h-index of 61 on Google Scholar. His leadership extends beyond academia as he serves as President of the Australasian Genomics Technologies Association (AGTA) and holds director positions at Pryzm Health and the National Centre for Indigenous Genomics (NCIG). Professor Dinger's research laboratory focuses on establishing new links between phenotype and genotype, particularly examining rare and complex diseases in relation to underexplored regions of the genome including pseudogenes, repetitive elements, non-canonical DNA structures, and noncoding RNAs. His work harnesses population-scale genomic datasets and sophisticated data science methods to bring an objective perspective to understanding how the genome stores information and how it is transacted in biology. His research interests span genomics, non-coding RNA biology, clinical applications of genomic medicine, and the development of computational approaches for analyzing complex genomic data. Analysis of Professor Dinger's recent publications reveals a strong emphasis on non-coding RNA research, particularly long noncoding RNAs and their roles in disease mechanisms. His work spans cancer genomics, neurological disorders, and fundamental genomic mechanisms including DNA secondary structures like i-motifs and G-quadruplexes. His research combines experimental approaches with advanced bioinformatics to address fundamental questions in genomic medicine and has significant translational implications for disease diagnosis and treatment. Highly Cited Researcher in Cross-Field category (2019, 2020, 2021) Fellow of the Faculty of Science (Research), Royal Society of Pathologists of Australasia (2016) NHMRC Career Development Award (2010) Queensland Government Smart Futures Fellowship (2009) Foundation of Research, Science and Technology New Zealand Postdoctoral Fellowship (2005) Professor Dinger has been instrumental in establishing and leading several significant research initiatives including Genome.One, one of the first companies globally to provide clinical whole genome sequencing services, and the Kinghorn Centre for Clinical Genomics at the Garvan Institute of Medical Research. His entrepreneurial experience includes founding four biotechnology and IT startups. He serves on multiple governance boards including the National Centre for Indigenous Genomics, focusing on using genomics to improve health outcomes for Australia's First Peoples. His laboratory at UNSW continues to advance our understanding of genomic regulation and its implications for human health and disease.

• Genomics
• Non-coding RNA research
• Phenotype-genotype relationships
• +7 more

Dr. Shixun Huang is a Lecturer in the School of Computing and Information Technology at the University of Wollongong, Australia. He holds a PhD from RMIT University and specializes in data mining, machine learning, and optimization algorithms for high-dimensional data problems. His research develops efficient algorithms for data discovery, similarity search, and network analysis. Current projects focus on optimized data acquisition strategies for machine learning, cost-effective labeling for graph neural networks, and cardinality estimation in high-dimensional databases. Methodologically, he combines combinatorial optimization with machine learning techniques. Dr. Huang supervises graduate research on diffusion models for medical imaging, graph prompt learning, and image captioning systems. His honors include multiple best paper awards at top database conferences and recognition for teaching excellence (College Top Course Award at RMIT). Recent publications address dataset distinctiveness maximization (WWW 2025), high-dimensional similarity search (VLDB 2025), and edge computing optimization (2024). Earlier foundational work established new approaches for influence maximization in social networks and temporal graph representation learning.

• Data Mining
• Machine Learning
• Algorithm Design
• +3 more

Dr. Honglei Xu is an Associate Professor of Industrial Optimization and Engineering at Curtin University, specializing in industrial system optimization for net-zero transition. He serves as Node Leader of ATN Industry Doctoral Training Centre and Mathematics Honours Coordinator. His research spans automation in mining, hybrid systems control, and optimization in construction and energy sectors. Recent publications demonstrate interdisciplinary approaches combining operations research, AI, and control theory for sustainable industrial solutions. Honors include IEEE Senior Membership and JSPS Fellowship. Current projects focus on public transport optimization, renewable energy forecasting, and intelligent control systems for mineral processing. Dr. Xu teaches courses in mathematical modeling and production planning while serving as associate editor for multiple international journals including Complexity and Energies.

• Industrial Net Zero Transition
• Automation in Mining
• Hybrid Systems Control
• +3 more

Ganeshsree Selvachandran is a Professor at the School of Business, Monash University Malaysia (MUM), where she conducts cutting-edge research in artificial intelligence, fuzzy mathematics, deep learning, and computational intelligence. She is actively involved in PhD supervision and leads significant research projects in biomedical imaging and forecasting. PhD in Pure Mathematics, Universiti Kebangsaan Malaysia (UKM), 2013 MSc in Pure Mathematics, Universiti Kebangsaan Malaysia (UKM), 2010 BSc (Hons) in Mathematics, Universiti Kebangsaan Malaysia (UKM), 2009 Her research focuses on fuzzy mathematics , deep learning , computational intelligence , and hybrid AI models , with applications in biomedical imaging (e.g., lung cancer detection), financial and environmental forecasting, and emotion recognition. She develops advanced fuzzy logic systems integrated with neural networks for real-world diagnostic solutions. The recent publications highlight a strong trend in applying deep convolutional neural networks and fuzzy logic to medical diagnostics, particularly in lung cancer and diabetic retinopathy detection. Her work increasingly combines generative models and hybrid AI for data augmentation and improved accuracy in healthcare applications. She also contributes comprehensive reviews in AI subfields, establishing foundational knowledge in reinforcement learning and medical AI. Scientific Awards and Recognitions: Ranked No. 6 globally in fuzzy sets/systems research by Scopus (2019) Member, Special Interest Group on Blue Economy, Malaysian Academy of Sciences (2020) Contributor to the Position Paper on Blue Economy (2022) Recipient of multiple national and international research awards Dr. Selvachandran has secured over MYR880,000 in research funding, including four Fundamental Research Grant Scheme (FRGS) grants, an industry grant from MTSFB, and a professorial chair grant from PNB. She mentors PhD students and collaborates on interdisciplinary projects. She serves as an editor for top journals like Engineering Applications of Artificial Intelligence and Computers and Electrical Engineering , and has reviewed over 150 papers for Web of Science/Scopus-indexed journals. She is involved in active research projects such as Advancing Lung Cancer Detection through Deep Fuzzy Convolutional Neural Networks and Sustainability Impact: Case Studies of UNGCMYB Member Companies . She also holds an adjunct professorship at Symbiosis International University (2023–2025), contributing to international academic collaboration.

• Artificial Intelligence
• Fuzzy Mathematics
• Deep Learning
• +12 more

Dr. Anthony Bartel is an Adjunct Industry Fellow at the School of Social Sciences, Media, Film and Education, Swinburne University of Technology. He teaches Physics, Electronic Instrumentation, and Multimedia Technology to undergraduate and postgraduate students across science, engineering, games, and multimedia disciplines. He has coordinated multiple subjects and courses in these areas. His educational background includes an undergraduate major in Physics, a Master's involving instrumentation for balloon-borne x-ray astronomy, and a PhD in neutron physics instrumentation development. Undergraduate: Physics Master's: Instrumentation for Balloon-Borne Astronomical X-Ray Observatory PhD: Instrument Development for Neutron Physics Experiments Dr. Bartel's research spans Artificial Intelligence, Curriculum and Pedagogy, Control Engineering, Mechatronics, Robotics, and Music Education Technology. His work emphasizes the use of AI and serious games to enhance deliberate music practice and self-regulated learning. He explores feedback models, incentive systems, and progression design in mobile music games to improve educational outcomes. His recent publications (2010–2021) demonstrate a strong trend toward integrating AI with music education, particularly in generating adaptive sight-reading exercises and analyzing percussive sounds. The research bridges computer science, education, and acoustics, focusing on intelligent tools for music practice and systematic sound description. He is available to supervise PhD students and has previously supervised doctoral research in intelligent music tools and percussive audio lexicons. Intelligent Tools for Deliberate Music Practice: Evolving Targeted Sight Reading Exercises (2015–2019) The Percussive Audio Lexicon (2014–2015) Dr. Bartel has not received any publicly listed scientific awards. His teaching and research are supported through institutional roles and collaborative projects in educational technology and instrumentation. He is involved in interdisciplinary teams working on serious games for music education and AI-driven learning systems, contributing expertise in instrumentation, signal processing, and pedagogical design.

• Artificial Intelligence
• Curriculum and Pedagogy
• Control Engineering
• +6 more

Associate Professor Phil Clausen is a computational biomechanics and wind energy expert at the School of Engineering, University of Newcastle . His career spans two major research themes: small wind turbine dynamics and fatigue testing and computational biomechanics of biological structures using finite element analysis (FEA). He has led the development of accelerated fatigue test programs for turbine blades and reverse-engineered iconic fossils like Smilodon fatalis and the Tasmanian Tiger to understand biomechanical evolution. Education: PhD and BEng (Hons) from University of Newcastle Research Expertise focuses on: Small wind turbine blade design, fatigue life prediction, and performance optimization Computational biomechanics of crocodiles, komodo dragons, and extinct species Publications (65+ journal articles) cover wind energy systems (2000–2020) and biomechanics (2005–2021), with high-impact work featured in Nature , PLoS ONE , and Royal Society B . Notable findings include debunking assumptions about sabre-toothed cat bite force and quantifying dingo vs. thylacine predatory mechanics. Grants ($1.06M+ total) include ARC Discovery Projects, industry partnerships with Aerogenesis Australia and TUNRA , and commercialization of diffuser-augmented turbine technology. He has supervised numerous research students and collaborated with interdisciplinary teams across engineering, biology, and paleontology.

• Small Wind Turbine Dynamics
• Computational Biomechanics
• Finite Element Analysis
• +3 more

Cara MacNish serves as an Associate Professor in the Department of Computer Science and Software Engineering at The University of Western Australia's School of Physics, Maths and Computing. Her academic role spans computational intelligence research and teaching with interdisciplinary applications in biomedical engineering and materials science. Her research expertise encompasses adaptive systems, artificial intelligence, neural networks, bioinformatics, cognitive science, evolutionary algorithms, machine learning, optimisation, and robotics. These converge in medical image processing (OCT denoising via GANs) and materials analysis (digital image correlation for displacement fields), reflecting her dual focus on algorithmic innovation and real-world engineering solutions. Recent publications demonstrate consistent advancement in two domains: deep learning applications for Optical Coherence Tomography enhancement (using GANs and deep feature loss) and novel digital image correlation techniques for materials science (handling discontinuities and nonlinear behavior). These areas show growing citation impact in medical imaging and fracture mechanics. MacNish has supervised 3 research students and secured 4 competitive grants, including Office for Learning & Teaching projects on engineering education (student experiences and gender inclusivity) and Defence Science and Technology Group funding for red teaming computational tools, alongside university research on evolutionary programming for code analysis. She maintains active collaborations across biomedical optics and materials engineering disciplines, evidenced by co-authorship with clinicians, computer scientists, and mechanical engineers on interdisciplinary projects addressing complex imaging and material behavior challenges.

• Adaptive systems
• Artificial intelligence
• Artificial neural networks
• +6 more

Dr Nataliia Sergiienko is a Senior Lecturer at the School of Electrical and Mechanical Engineering, University of Adelaide. Her research focuses on wave energy conversion, floating offshore wind turbines, and hybrid renewable energy systems, with a particular emphasis on power optimization, coastal erosion mitigation, and autonomous underwater vehicles (AUVs). Research Interests: Wave energy converter design and control Hybrid wind-wave energy systems Coastal protection using offshore wave farms Deep learning for marine energy forecasting Hydrodynamic optimization of floating structures Supervision & Collaboration: Dr Sergiienko co-supervises multiple Honours and PhD projects with colleagues like Prof Ben Cazzolato and Prof Maziar Arjomandi, focusing on floating wind turbines, distributed propulsion aircraft, and CubeSat technology.

• Wave Energy Conversion
• Floating Offshore Wind Turbines
• Hybrid Wind-Wave Energy Systems
• +3 more

Associate Professor Fatemeh Vafaee is a leading researcher at the University of New South Wales (UNSW) , holding appointments as Associate Professor in the School of Biotechnology and Biomolecular Sciences (BABS) and Deputy Director (Science) of the UNSW AI Institute . She previously served as Deputy Director of the UNSW Data Science Hub (uDASH) and has held academic positions at the University of Toronto and the University of Sydney. PhD in Artificial Intelligence from University of Illinois at Chicago Postdoctoral Fellowships at University of Toronto and University of Sydney Founded the AI-Enhanced Biomedicine Laboratory in 2017 Her research focuses on deploying advanced AI techniques to address biomedical challenges through: Biomarker Discovery for cancer and neurodegenerative diseases Single-Cell Multi-Omics data integration and analysis Computational Drug Repositioning and network pharmacology Multi-Omics Data Fusion and temporal network modeling Recent publications demonstrate expertise in liquid biopsy development , single-cell imaging , and AI-driven cancer diagnostics . Her methodological contributions include novel deep learning architectures for omics data analysis and graph neural networks for drug synergy prediction. Scientific accolades include: Winner, Women in AI Asia-Pacific Health Award (2023) Runner-Up, WAI-APAC Innovator of the Year (2023) Top 10 Women in AI in Asia-Pacific (2023) Australian Bioinformatics and Computational Biology Society Research Excellence Award (2023) She supervises PhD candidates across computational biomedicine and AI in healthcare , with significant grant achievements exceeding $17M in competitive funding, including schemes from ARC Discovery , NHMRC , and Medical Research Future Fund .

• Artificial Intelligence
• Machine Learning
• Systems Biology
• +5 more

Dr. Torsten Reiners is a Senior Lecturer at Curtin University's School of Management and Marketing, part of the Faculty of Business and Law. His research focuses on Supply Chain Management, Operations Optimization, Emerging Technologies (IoT, Mixed Reality), and Decision Support Systems. He holds a PhD in Information Systems/Education and has expertise in programming and development related to virtual reality and IoT. Key research areas include end-to-end supply chain transparency, urban logistics, cyber-physical systems, and gamification in training. His work bridges Computer Science and Business, with over 50 publications in journals like International Journal of Production Economics and Australasian Journal of Information Systems . He has led projects funded by OLT grants and industry partnerships, including the nDiVE VR training environment for Logistics education. Reiners' contributions span gamification design, virtual world pedagogy, and sustainable supply chain analytics. His current projects explore IoT adoption impacts and agricultural logistics optimization. He advises on digital ecosystems, geospatial analytics, and mental health systems in pandemic contexts.

• Supply Chain Management
• Logistics and Operations
• Decision Support Systems
• +5 more

Dr. Michael Leonard is an Associate Professor in the School of Architecture and Civil Engineering at the University of Adelaide, affiliated with the Environmental Engineering department. His research focuses on applying numerical and statistical models to environmental engineering challenges, particularly flood risk assessment, hydrological modeling, and water security. He has expertise in stochastic rainfall modeling, multivariate extremes, and climate change impacts. Dr. Leonard’s work emphasizes understanding hazardous environmental systems and improving flood risk management strategies. He is eligible to supervise Masters and PhD students in these areas. His research interests include flood modeling, climate adaptation, and sustainable water resource management. He has contributed to projects such as coastal flood risk analysis, brackish groundwater desalination, and global hydrological change detection. His recent articles highlight trends in flood magnitude, rainfall extremes, and the integration of climate indices to predict hydrological extremes. He has also explored the implications of climate change on water distribution systems and infrastructure resilience. Dr. Leonard collaborates on initiatives like the Global Streamflow Indices and Metadata Archive (GSIM), advancing big data applications in hydrological analysis. His work bridges theoretical models with practical engineering solutions, addressing real-world challenges in water security and environmental sustainability.

• Water Engineering
• Hydrologic Modelling
• Hydraulic Modelling
• +5 more

Dr. Aaron Zecchin is a Senior Lecturer in Environmental Engineering at the University of Adelaide, affiliated with the School of Architecture and Civil Engineering under the Faculty of Sciences, Engineering and Technology. His research focuses on fluid network dynamics, hydraulic system modeling, and evolutionary algorithm applications in engineering problems. His expertise includes transient analysis in pipeline networks, fault detection using statistical methods, and optimization techniques for water distribution systems. He actively investigates fluid-structure interactions, Laplace-domain characterizations, and sparse linear solvers for steady-state simulations. His work integrates advanced computational methods with practical engineering challenges, emphasizing infrastructure resilience and sustainability. Research Themes: Hydraulic transients, pipeline condition assessment, system identification Key Tools: Method of characteristics, Kalman filtering, ant colony optimization Applications: Water distribution systems, flood mitigation, wildfire risk modeling Recent publications emphasize anomaly detection in pipe systems, burn probability prediction using dimensionality reduction, and optimizing municipal infrastructure rehabilitation. His work bridges theoretical modeling with real-world infrastructure challenges, contributing to safer and more sustainable engineering solutions.

• Fluid network dynamics
• Partial differential equation solvers
• Hydraulic network characterization
• +4 more