Dr. Shan Huang is a Researcher at the School of Engineering, University of Newcastle. She holds a BE in Civil Engineering from Hunan University of Science and Technology, an MS in Road and Railway Engineering from Central South University, and a PhD in Civil Engineering from the University of Newcastle. Her research focuses on computational geomechanics and probabilistic geotechnics, particularly in soft soil consolidation and geotechnical risk assessment. Dr. Huang's work integrates numerical simulation and advanced probabilistic methods, with notable contributions to Bayesian back analysis for settlement prediction and parameter calibration in soft soils. Her research has been published in journals such as Computers and Geotechnics , ASCE Journal of Geotechnical and Geoenvironmental Engineering , and Soils and Foundations . Key projects include the analysis of embankments in Ballina, Australia, where she applied Bayesian methods to predict long-term settlements using monitored data. Her work emphasizes computational efficiency and practical applications in geotechnical engineering.
Dr. Eila Erfani is a Senior Lecturer at the School of Information Systems and Technology Management (SISTM) within the UNSW Business School. She leads the Cybersecurity Management major and Business Analytics programs. Dr. Erfani holds a PhD in Information Systems from Macquarie University, alongside a Master of Systems Engineering and Bachelor of Engineering. Her research focuses on ethical AI integration, cybersecurity governance, and leveraging machine learning for sustainability challenges. Notable areas include: AI ethics in healthcare and finance IoT-driven safety systems Open innovation platforms Big data analytics for sustainable supply chains Publications span applications of machine learning in lithium markets, bioleaching optimization, and drowning detection technologies. She has also extensively studied social media's psychological impacts, including addiction risks and mental health consequences. Recent work includes developing mHealth applications for senior wellness and cancer patient support. Her teaching emphasizes bridging technical capabilities with ethical responsibility, preparing students for roles in cybersecurity management and data-driven decision-making. Current research explores AI's role in achieving UN Sustainable Development Goals through triple-bottom-line approaches.
Dr Shahnewaz Ali is a Postdoctoral Fellow in the Faculty of Engineering's School of Electrical Engineering & Robotics at Queensland University of Technology (QUT). He is affiliated with the Centre for Robotics and holds a PhD from QUT and an MSc in Computer Engineering from Politecnico di Milano. His research focuses on the intersection of robotics, artificial intelligence, and biomedical applications, emphasizing wearable technology, surgical robotics, and sensor systems. Key research interests include AI-driven medical imaging, robotic surgery systems, and biomarker detection using wearable devices. His work spans sensor technologies for robotics, real-time surgical scene analysis, and predictive performance monitoring. Recent projects include developing a wearable monitoring system for stress biomarkers and advancing arthroscopic surgical techniques through 3D mapping and deep learning. Education: PhD, Queensland University of Technology MSc (Computer Engineering), Politecnico di Milano Dr Ali's publications cover topics like surgical scene restoration, microRNA-based performance prediction, and sensor development for medical robotics. His research bridges engineering and healthcare, aiming to enhance diagnostic accuracy and robotic surgical precision. Collaborations include multidisciplinary teams at QUT and international institutions.
Dr. Sam Bakhtiari is a Research Fellow at Curtin University's Curtin Corrosion Centre, affiliated with the WASM: Minerals, Energy and Chemical Engineering School under the Faculty of Science and Engineering. His research focuses on materials science, metallurgy, and corrosion engineering, with particular emphasis on NiTi-based shape memory alloys, phase transformations, and corrosion mechanisms. He applies data science techniques, such as machine learning models, to study materials degradation and stress corrosion cracking. Dr. Bakhtiari's work bridges experimental and computational approaches, exploring topics like amorphization in nanocomposites, deformation-induced martensite stabilization, and additive manufacturing of metals. He collaborates on projects involving superalloys and metallic glasses, emphasizing their mechanical properties and structural integrity. His contributions span journals like Shape Memory and Superelasticity , Acta Materialia , and Journal of the Electrochemical Society . Key research trends in his publications include: Phase behavior and defect engineering in NiTi alloys. Predictive modeling of corrosion processes using XGBoost and electrochemical data. Design of functionally graded materials for tailored mechanical responses. No scientific awards are listed. His affiliations include the Curtin Corrosion Centre, and he maintains an ORCID profile (https://orcid.org/0000-0002-8808-8427). Experimental and computational datasets underpin his work, with a focus on materials innovation for engineering applications.
Dr. Yuanhong Chen is a Postdoc Researcher at the Australian Institute for Machine Learning (AIML) within the University of Adelaide's Division of Research and Innovation. His work focuses on advancing computer vision, multimodal learning, and generative models, particularly in medical image analysis and audio-visual perception. He explores integrating large language models for cross-modal understanding to enhance AI systems' interpretability and robustness. Research interests include: Interpretable AI frameworks for medical imaging Semi-supervised and self-supervised learning techniques Audio-visual contextual learning and binaural audio generation Applications in breast cancer screening and disease classification Recent publications highlight innovations in prototype-based learning for medical diagnosis, cross-modal segmentation, and unsupervised anomaly detection. His work on Braix risk score and AutoCumulus demonstrates contributions to automated medical biomarker development. Collaborations within AIML and interdisciplinary projects at the University of Adelaide underscore his commitment to bridging foundational AI research with real-world healthcare applications.
Professor Heike Ebendorff-Heidepriem is a Professor in the School of Physics, Chemistry and Earth Sciences at the University of Adelaide. She serves as Deputy Director of the Institute for Photonics and Advanced Sensing (IPAS) and Director of the Optofab Adelaide Hub within the Australian National Fabrication Facility (ANFF). Her research focuses on advanced optical fiber fabrication, materials science, and photonics applications, including high-precision glass extrusion, exposed-core fibers, and quantum sensing. She leads a multidisciplinary team of over 20 researchers and has secured >$20M in funding, collaborating with global industry and academic partners. Her work spans fiber lasers, mid-infrared photonics, and sensor technologies, with significant contributions to nanomaterial integration and plasmonic processes. She has supervised 12 PhD and 5 HDR students over 15 years, emphasizing mentorship in photonics and materials innovation. Research Interests: Professor Ebendorff-Heidepriem specializes in developing novel optical fibers and glasses for applications such as high-power laser delivery, nonlinear optics, and evanescent field sensing. Her team pioneers fabrication techniques like billet extrusion and ultrasonic milling, enabling structures like suspended nanowire-core fibers and diamond-doped sensors. Key areas include mid-infrared materials (tellurite, fluoride glasses), plasmonic nanoparticle embedded glasses, and portable sensing solutions for mining and biomedical fields. Labs/Teams: IPAS and Optofab Adelaide Hub provide advanced facilities for fiber fabrication, laser development, and materials characterization. Her collaborations include 82 university groups and 30 industry/defense partners, driving commercialization of technologies like gold nanoparticle-based colored glass and diamond-doped fiber sensors.
Dr. Mehar Khatkar is a senior researcher at the University of Adelaide's School of Animal and Veterinary Science, part of the Faculty of Sciences, Engineering and Technology. His work focuses on integrating genomics, AI/ML, and phenomics to enhance livestock productivity and aquaculture through precision farming techniques. He has extensive experience across species like cattle, sheep, shrimp, and poultry. Research Interests: Smart farming applications using high-throughput phenomics and genomic data Advanced breeding strategies leveraging genomic information Whole genome prediction for health and performance traits Gene discovery via association studies and selection signatures He is eligible to supervise Masters and PhD students but no specific advisees are listed. No scientific awards or recent publications are explicitly detailed in the provided text.
Dr. Quazi Mamun is an Associate Professor and Higher Degree Research (HDR) Coordinator at the School of Computing, Mathematics and Engineering, Charles Sturt University (CSU). With over 23 years of experience, he is a leading researcher in cybersecurity, AI-driven security, IoT, blockchain, and distributed systems. PhD in Distributed Computing (Monash University, 2007–2011) MSc in Global Information and Telecommunication Studies (Waseda University, Japan) BSc in Computer Science and Engineering (Bangladesh University of Engineering and Technology) His research focuses on AI-driven cybersecurity (machine learning models for threat detection), blockchain and IoT security (lightweight cryptography), and secure smart environments (AI-based smart cities). Recent publications highlight trends in blockchain data retrieval , cross-domain adversarial attacks , and sensor network integration with healthcare . Key research grants include: $5M – Japan Science and Technology Agency (2024–2029) $120K – Connectivity Innovation Network (2023–2026) $180K – Cyber Security CRC (2020–2023) $25K – GSK Global (2022–2023) Scientific honors include: Vice-Chancellor's Award for Excellence (2016) Excellence Award (Research) (2024) Best Paper Awards at IEEE/ACM conferences (2017, 2022) Innovation Networks grant for IoT security (2023) He oversees research groups such as the Data Mining Research Group (DaMRG), Cyber Security Research Group (CSRG), and Advanced Network Research Group (ANRG), while mentoring 8 PhD students and delivering keynote speeches globally.
Kok-Leong Ong is a Professor of Business Analytics at RMIT University's College of Business & Law, where he serves as Director of the CoBL Technology Initiative, Director of the Enterprise AI and Data Analytics Hub, and Head of the Department of Information Systems and Business Analytics. With over $1.4 million in research grants, he specializes in translating analytics and machine learning into practical business applications across multiple verticals including e-Commerce, public health, sports, urban studies, marketing, and learning. His research has consistently ranked in the top 25% and 5% of works in their domains according to Altmetric. Professor Ong's research spans Business Analytics, Artificial Intelligence, Machine Learning, and Information Systems, with a focus on making data actionable through analytics-2-business translation, automation, and applications. His work addresses critical challenges in cybersecurity for wearable health devices, ECG-based authentication systems, federated learning privacy, carbon accounting for maritime transport, and AI-driven solutions for vehicle damage detection. He has developed frameworks for operationalizing analytics in business contexts and has made significant contributions to mHealth applications for infant care and breastfeeding support. Among his notable scientific achievements, Professor Ong has received two VC's Teaching Awards and was named one of Australia's Leading Data Academics by CDO Magazine in 2021. He has secured over $1.4 million in research funding and serves on prestigious conferences including KDD and PAKDD. His research has been recognized for its high impact, with many works ranking in the top percentiles of their respective fields. Professor Ong actively supervises numerous research students across diverse topics including human-aligned AI, securing LLMs for financial applications, cyber risks in enterprise AI systems, ECG authentication security, and AI transformation for SMEs. He previously played a key role in establishing Australia's first Business Analytics degree and led La Trobe Business School's analytics program from 2015 to 2019 before joining RMIT in September 2021. Through the Enterprise AI and Data Analytics Hub and his role with RMIT University's Digital3 board, Professor Ong leads initiatives focused on bridging the gap between advanced analytics capabilities and business value creation. His work emphasizes practical implementation of AI and analytics solutions that address real-world challenges across multiple industry sectors.
Mirana Ramialison is a Professor at Monash University, affiliated with both the Australian Regenerative Medicine Institute and Victorian Heart Institute (VHI). She maintains an active research program with significant contributions to cardiovascular development and regenerative medicine. Her research interests focus on understanding the role of non-coding DNA regulatory elements in heart development and disease, formation of boundaries in developing embryos, and molecular mechanisms underlying cardiac function. She employs cutting-edge genomic, computational, and imaging approaches to investigate gene regulatory networks that govern cardiovascular development. Her recent publications demonstrate expertise in spatial transcriptomics, machine learning applications in epigenomics, and the use of vertebrate models like the African killifish to study aging processes. Her work spans developmental biology, genomics, and computational approaches to understand heart development and disease mechanisms. Professor Ramialison leads multiple significant research projects including 'Role of human non-coding DNA regulatory elements (REs) in heart development and disease' as Primary Chief Investigator, and collaborates on projects related to chemotherapy-induced heart damage prevention and bone growth mechanisms. She has produced 71 research outputs with notable recent publications in high-impact journals including Nature Cardiovascular Research, Genome Biology, and Communications Biology, demonstrating sustained scholarly productivity and impact in her field.
Dr. Jessica Korte is a Senior Lecturer in Computer Science at the School of Computer Science, Faculty of Science, Queensland University of Technology (QUT). Her research lies at the intersection of human-computer interaction, accessibility, and inclusive design, with a strong emphasis on participatory and co-design methodologies involving children and Deaf communities. She specializes in Australian Sign Language (Auslan) technologies and the development of assistive systems using AI and user-centered approaches. Her research interests include: Participatory and co-design with children and marginalized communities Accessibility and assistive technologies for Deaf and disabled individuals Auslan-based interfaces and virtual avatars Human-centered AI and machine learning integration Ethical considerations in distributed design Software development for inclusive systems The analysis of her recent publications reveals a consistent focus on inclusive technology design, particularly through co-design with children and Deaf participants. Her work spans real-world applications such as public transport announcements and smart home systems using Auslan, as well as methodological contributions in distributed participatory design during global challenges like the pandemic. She frequently publishes in top venues including CHI, IDC, and ASSETS. Dr. Korte has not been mentioned to have received any specific scientific awards in the provided text. She is actively involved in supervision and research collaboration, though specific names of students or grants are not listed. She contributes editorially to journals in child-computer interaction and leads projects that transition research participants into co-researchers, emphasizing empowerment and inclusivity. Her work is associated with initiatives involving VR prototyping, animated Auslan avatars, and co-designed public systems, indicating active engagement with interdisciplinary teams and real-world implementation of accessible technologies.
Paul Wu is an Associate Professor in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science, where he also serves as an industry research fellow in the strategic partnership between the Centre for Data Science (CDS) and AIS/QAS. He leads the sports systems domain within CDS and applies statistical and machine learning models to complex systems across sports, marine science, and defence sectors. PhD, Queensland University of Technology Master of Engineering Science (Computer and Comm Engineering), Queensland University of Technology Bachelor of Engineering (Electrical and Computer Engineering), Queensland University of Technology His research focuses on Bayesian statistics, dynamic Bayesian networks, state space modelling, and simulation techniques. He works closely with domain experts to solve real-world problems in sports performance, ecological resilience, and human systems. His interdisciplinary work spans sports science , marine ecology , and defence applications . Paul’s recent publications demonstrate a strong trajectory in predictive analytics for elite sports and ecosystem modelling, particularly using Bayesian frameworks. His work on swimming performance prediction has informed national training strategies and contributed to competitive success. He has also advanced methods in clustering, model adaptation, and psychosocial risk assessment. His scientific contributions have been recognized through impactful collaborations with elite sports organizations including the Australian Institute of Sport, Swimming Australia, and the West Coast Eagles. Testimonials highlight his role in transforming data analytics in sports injury recovery and performance optimization. Applied Bayesian models in elite sports decision-making Developed predictive tools for marine ecosystem resilience Collaborated on over 30 industry and government projects Supervises research in complex sports data analytics Paul leads a dynamic research environment focused on translating statistical innovation into practical impact across diverse domains.
Dr Nguyen Tran is a Researcher at the School of Computer and Mathematical Sciences , part of the Faculty of Sciences, Engineering and Technology at the University of Adelaide . His work focuses on developing software systems that are correct, efficient, and secure, particularly through the application of blockchain and related technologies. Nguyen Tran's research spans four key domains: Software Engineering for large-scale systems, Cryptography & Blockchain for security, Internet of Things for physical-digital integration, Machine Intelligence for automation. He is eligible to co-supervise Masters and PhD students and contributes to the Centre for Research on Engineering Software Technology . His publications highlight advancements in blockchain metadata management, IoT search engines, and decentralized provenance systems.
Dr. Hendra I Nurdin is a Senior Lecturer in the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW), where he has been a faculty member since 2012. His academic background spans Electrical Engineering and Applied Mathematics with a focus on systems and control theory, with significant research intersections in quantum physics and power engineering. He serves as an Associate Editor for IEEE Control Systems Letters (IEEE L-CSS) starting from January 2024. Dr. Nurdin's research spans quantum systems and control, nonlinear control of microgrids, and neuromorphic computing for nonlinear systems. His work in quantum control intersects with quantum physics while his research on microgrid control intersects with power engineering. His research has resulted in numerous publications across prestigious journals including Nature Communications, Physical Review Research, IEEE Control Systems Letters, and IEEE Transactions on Automatic Control. His recent publications (2020-2025) predominantly focus on quantum reservoir computing, quantum parameter estimation, control of non-Markovian quantum systems, and applications of control theory to energy systems. The research demonstrates a clear trajectory toward practical implementations of quantum computing concepts and advanced control methodologies for both quantum and classical systems. Associate Editor for IEEE Control Systems Letters (IEEE L-CSS) from January 2024 Dr. Nurdin has supervised several PhD students to completion and currently mentors PhD candidate Wen Liu. His research group has produced significant work in quantum control systems, with former students including Wenxing Li, Yihuan Liao, Jiayin Chen, Jiacheng Li, Muhammad Ali, Zhan Shi, and Onvaree Techakesari. He actively seeks new students with strong academic backgrounds for research in his areas of interest, particularly in quantum systems and control. His laboratory work focuses on quantum systems control, quantum reservoir computing, and microgrid control systems. He has developed courses including ELEC9782 Special Topics in Electrical Engineering 2 (Quantum Control) and teaches ELEC4631 Continuous-Time Control System Design.
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.
