جست‌وجوی استاد و پژوهشگر

Erandi Hene Kankanamgeمشاهده پروفایل

مدرس ارشد

• Multi-agent systems
• Computational intelligence
• Multi-objective optimisation
• +۴ مورد دیگر

Erandi Hene Kankanamge is a Senior Lecturer in Computer Science at the University of New South Wales, Canberra (UNSW Canberra), working within the School of Systems & Computing. Her academic career spans over a decade with significant contributions to artificial intelligence research and education. Her educational background includes: BSc (hons) in Computer Science at the University of Colombo, Sri Lanka (First Class) PhD in Computer Science at UNSW Canberra (2014) Dr. Kankanamge's research focuses on the intersection of artificial intelligence and human-computer interaction, with expertise in multi-agent systems, computational intelligence, multi-objective optimization, human-computer interfaces, and games for health. Her work bridges theoretical AI advancements with practical applications in defense, healthcare, and gaming contexts. She employs computational intelligence approaches to model complex behaviors and develop systems that enhance human performance and wellbeing through digital interactions. Her publication record shows a clear evolution from foundational work on behavior modeling to sophisticated applications in human-swarm interaction, trust architectures, and exergaming interfaces. The research demonstrates consistent emphasis on practical implementations of AI techniques in real-world scenarios, particularly in board games, military applications, and health-focused digital experiences, with strong interdisciplinary connections between computer science, psychology, and health sciences. Dr. Kankanamge has secured significant research funding through multiple competitive grants: Model Abstractions for Military Wargaming ($220,000, DSTG) Actionable Cyber Security Intelligence from Big Data ($75,000, Industry Grant) Simulation and Visualisation Using Data Farming ($350,000, DSTG) Game Master: Improved player mental and physical wellbeing ($50,000, Rector's Start-up Grant) As an academic advisor, Dr. Kankanamge supervises multiple PhD candidates across diverse research topics including human-swarm interaction, motive profile compositions for cooperative teams, stress detection during gameplay, and reinforcement learning for complex board games. Her professional service includes roles as Secretary of IEEE CIS ACT Chapter, conference organization roles, and journal reviewing activities across multiple prestigious publications in AI and gaming. Dr. Kankanamge's research contributes to multiple interdisciplinary teams focusing on AI-enabled analysis, human-machine teaming, and serious games development. Her work with the Defence Science and Technology Group demonstrates strong connections between academic research and defense applications, while her interest in games for health shows commitment to societal impact through technology.

• Natural Language Processing
• AI Fairness
• Knowledge Representation & Reasoning
• +۳ مورد دیگر

Dr. Sebastian Binnewies is a Senior Lecturer at Griffith University's School of Information and Communication Technology (ICT) and serves as Director of the Griffith App Factory. With over $3M in external grant funding, his research focuses on Natural Language Processing, AI Fairness, and Knowledge Representation & Reasoning, with applications in healthcare, climate resilience, and education. Senior Lecturer (2021–present) Lecturer (2018–2021) Associate Lecturer (2016–2018) PhD (2011–2015) & Honours Bachelor's in Information Technology (2009–2011) from Griffith University His research addresses Natural Language Processing for bias mitigation, AI Fairness in decision systems, and Knowledge Representation for healthcare applications. Key projects include: Individualised heat-health early warning systems (Wellcome Trust $2.36M) SurgeImpact decision support system ($2.09M QRRRF) Vascular Access Passport apps ($16.8K–$99.3K grants) Social media analytics for public health and education Recent publications explore: Time-series forecasting with dynamic window optimisation Personalised abusive language detection via LLMs IT service well-being in educational ecosystems Heatwave policy influence through the Ethos Project Scientific contributions include: 2025: Nature Climate Change publication 2024: IEEE Transactions on Learning Technologies 2023: Experimental research on heat-induced hyperthermia in aging populations 2021: Student Experience of Teaching accolades As a supervisor, he guides doctoral research in: UN SDGs integration in remote work Deep learning for cryptocurrency prediction Commonsense reasoning in question answering Feature-enhanced neural networks for topic modeling Laboratory leadership includes: Directing Griffith App Factory (student enterprise for real-world IT projects) Membership in Institute for Integrated and Intelligent Systems (2016–2024)

• Structural Mechanics
• Sustainable Construction
• Recycled Waste Materials in Concrete
• +۳ مورد دیگر

Dr. Yanni Bouras is a part-time Lecturer at the College of Sport, Health & Engineering , Victoria University, with a PhD in Structural Engineering (2020) and BEng (Hons) (2015). His research focuses on structural mechanics, sustainable construction, and waste material utilisation in concrete composites. Education BEng (Hons), Victoria University (2015) GCTE, Victoria University (2020) PhD, Victoria University (2020) Research Interests Yanni investigates the thermal stability of concrete structures, sustainable infrastructure rating tools, and kraft fibre reinforcement from waste cardboard in concrete composites. His work bridges structural engineering with environmental sustainability. Recent Research Trends His publications since 2020 highlight innovations in waste valorisation (cardboard, coffee cups), machine learning for concrete creep prediction, and fractional calculus-based modeling. Key themes include low-carbon concretes , lifecycle assessments , and multi-objective optimisation . Supervision & Grants Yanni serves as a joint supervisor for research on engineered cementitious composites. He contributed to the Social Value Creation for Transport and Infrastructure grant (2021–2022), focusing on sustainable infrastructure. Institute Affiliation He is affiliated with the Institute for Sustainable Industries & Liveable Cities at Victoria University, aligning his work with UNSDGs 12, 6, 7, and 9.

Ian Woodمشاهده پروفایل

مدرس

• Statistics
• Machine Learning
• Bioinformatics
• +۷ مورد دیگر

Dr. Ian Wood is a Lecturer in Statistics at the School of Mathematics and Physics within the Faculty of Science at The University of Queensland. He received his PhD from the same institution in 2004. His teaching responsibilities include instructing 4th-year advanced statistics, 1st-year pharmacy students, and postgraduate data science courses. Dr. Wood's research spans multiple interdisciplinary domains including: Statistical methodologies for classification and mixture models Machine learning algorithms and stochastic optimization techniques Bioinformatics applications in genomics and medical research Evolutionary computation and optimization frameworks His work frequently bridges theoretical statistics with practical applications in biomedicine and computational biology. Analysis of Dr. Wood's recent publications reveals three primary research trends: Advanced algorithm development for statistical modeling and optimization Applications in biomedical contexts including cancer research, tissue engineering, and pathogen genomics Methodological innovations in machine learning and computational statistics His research consistently demonstrates strong interdisciplinary collaboration across statistics, computer science, and life sciences. Dr. Wood maintains an active supervision portfolio, serving as principal advisor for PhD candidates working on: Evolutionary algorithms for variable-length problems MRI brain segmentation using Markov random fields Estimation of distribution algorithms He also serves as associate advisor for multiple doctoral and master's projects spanning statistics, optimization, and bioinformatics. Dr. Wood is affiliated with the Centre for Organic Photonics and Electronics and maintains collaborations across multiple research teams focused on statistical applications in health sciences and engineering.

Jia Guoمشاهده پروفایل

پژوهشگر

• Operations research
• Optimisation
• Modelling and simulation
• +۱۸ مورد دیگر

Dr. Jia Guo is a Researcher at the Research Centre for Integrated Transport Innovation (rCITI) within the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). She received her Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin, USA. Her research spans multiple domains including transportation systems, network design, healthcare operations, and power systems optimization. Her primary research interests include: Operations research and optimization methodologies Mathematical modeling and simulation Machine learning applications in transportation Rail and air transportation systems Stochastic analysis and modeling Dr. Guo's work focuses on developing advanced optimization techniques to solve complex problems in transportation planning and logistics. Her research has produced significant contributions in freight train planning, air traffic controller scheduling, vehicle routing, transportation and logistics design, travel behavior analysis, and staff scheduling. She has extensive expertise in mathematical modeling, decomposition algorithms, metaheuristics, machine learning methods, and statistical analysis. Her publications reflect a strong trajectory in applying operations research to real-world transportation challenges, with a particular emphasis on stochastic and optimization approaches across rail, air, and urban transportation systems. Dr. Guo maintains an active research profile with an ORCID identifier (https://orcid.org/0000-0002-5350-2595) and is engaged with the academic community through Google Scholar.

• Optimization
• Numerical Analysis
• Applied Mathematics
• +۴ مورد دیگر

Chayne Planiden is a Senior Lecturer in the School of Mathematics and Applied Statistics at the University of Wollongong, where he has been appointed since 2022. His research focuses on mathematical optimization, particularly nonsmooth optimization techniques including regularization methods, derivative-free algorithms, and the VU-algorithm. He specializes in proximal mappings, simplex gradient approximations, and Hessian approximation methods. Dr. Planiden's research spans both theoretical and applied mathematics, with significant applications in energy market modeling. His work addresses renewable energy integration, microgrid management, and electricity market design using optimization frameworks. Key methodologies include derivative-free optimization techniques and numerical analysis approaches for complex systems. In terms of research supervision, Dr. Planiden currently mentors three PhD students working on projects involving optimization algorithms, reaction-diffusion equations, and financial option pricing. He has successfully supervised one PhD candidate to completion on decarbonized electric grid frameworks. No scientific awards or honors are mentioned in the available information.

• Artificial Intelligence
• Machine Learning
• Operations Research
• +۵ مورد دیگر

Dr. Yuan Sun is a Lecturer in Business Analytics and Artificial Intelligence at La Trobe University's La Trobe Business School. His research focuses on leveraging machine learning for combinatorial optimization, including problem reduction methods and hybrid algorithms. He has contributed to top-tier journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and conferences such as ICML and NeurIPS. Sun collaborates with researchers from institutions like Monash University and Singapore Management University, and is affiliated with the ARC Training Centre in Optimisation Technologies (OPTIMA). He co-organized the ACM GECCO 2024 conference and serves on program committees for major AI conferences. Academic Position: Lecturer at La Trobe University (2022–present) Education: PhD in Artificial Intelligence (University of Melbourne), BSc in Applied Mathematics (Peking University) Research interests span machine learning, operations research, and optimization, with notable work on integrating AI with digital twins for sustainable power grids and federated learning frameworks like F3KM. Sun's methods enhance constraint programming and ant colony optimization through supervised learning, addressing complex real-world problems. Grants and collaborations include the 'Quantum Enhanced Optimisation for Energy Efficient Data Centres' project and consulting for the Australian mining workforce analysis. Teaching roles include Algorithms & Analysis and Evolutionary Computing, with joint supervision of PhD projects on combinatorial optimization and mixed-integer programming.

Frank Neumannمشاهده پروفایل

استاد

• Artificial Intelligence
• Bio-inspired Computing
• Cybersecurity
• +۳ مورد دیگر

Frank Neumann is a Professor in the School of Computer Science at The University of Adelaide. He leads the Optimisation and Logistics group and holds affiliations with the University of Melbourne as an Honorary Professorial Fellow. His expertise spans artificial intelligence, bio-inspired computing, and optimization. He has held leadership roles, including interim Head of School and Associate Dean (Research) in the Faculty of Engineering, Computer and Mathematical Sciences. Education: PhD in Computer Science (2006), Diplom (2002) from Christian-Albrechts-University of Kiel, Germany. Postdoctoral research at Max Planck Institute for Informatics (2006-2010). Research interests focus on evolutionary computation, cybersecurity, renewable energy optimization, and supply chain management. He has contributed to foundational theory in randomized search heuristics and practical applications in logistics and healthcare. Recent projects include NHMRC grants on personalized time-use interventions and ARC-funded initiatives in optimization and complex resource management. He has authored influential books on bio-inspired computation and theoretical evolutionary algorithms. Scientific Awards: ARC Future Fellowship, Humboldt Fellowship, NHMRC Grants. Grants: ARC Industrial Transformation Training Centre, Research Consortia Program.

Anju Johamمشاهده پروفایل

پژوهشگر ارشد

• Polycystic Ovary Syndrome
• Obesity
• Women's health
• +۲ مورد دیگر

A/Prof. Anju Joham is a Senior Research Fellow and NHMRC Fellow at Monash University's Monash Centre for Health Research & Implementation, leading the PCOS epidemiology research group. She also serves as a Visiting Endocrinologist at Monash Health, Melbourne. Her academic journey includes a PhD (2015) and FRACP (2010) from Monash University and the Royal Australasian College of Physicians, respectively. Key Roles: Senior Research Fellow, NHMRC Fellow, Deputy Lead of 2023 International PCOS Guidelines Awards: NHMRC Early Career Fellowship, NHMRC CRE WHiRL Fellowship Grant Her research focuses on Polycystic Ovary Syndrome (PCOS) , obesity , metabolic health , and women's health , with a particular emphasis on epidemiological studies using longitudinal cohorts like the Australian Longitudinal Study on Women’s Health (ALSWH). Her work has driven initiatives such as the statewide PCOS Service at Monash Health (2018) and informed international clinical guidelines. Recent publications highlight diagnostic accuracy of androgen measurements in PCOS, diabetes management in women with PCOS, and global health challenges like diabetes self-management in rural Bangladesh. Grants & Projects: Leads or co-invests in 15 projects, including the 'Digital health for optimising evidence implementation in women's health' (2023–2028) and 'Advancing Women in Healthcare Leadership' (2023–2027). Labs/Teams: Central to the CRE WHiRL (Centre for Research Excellence in Women’s Health in Reproductive Life), collaborating internationally on PCOS and women's health initiatives.

• Numerical Geophysics
• Groundwater Modelling
• Mantle Convection
• +۳ مورد دیگر

Dr. Sia Ghelichkhan is a Tenure-Track Lecturer at the Research School of Earth Sciences (RSES) and member of the Institute for Water Futures at ANU. She holds a PhD in Geophysics from Ludwig Maximilian University of Munich (2019). Her expertise lies in numerical geophysics, particularly in developing large-scale models for groundwater systems, mantle convection, and glacio-isostatic adjustment. As co-lead of the Geoscientific Adjoint Optimisation Platform (G-ADOPT), she integrates advanced numerical methods to address Earth system dynamics. Her research combines adjoint data assimilation techniques with observational data to constrain mantle flow and geodynamic processes. Research interests focus on continental-scale groundwater modelling, mantle rheology, and dynamic topography. She leads projects on mantle convection retrodictions (e.g., early Cenozoic Atlantic region) and collaborates on global geodynamic models. Supervised student: Ruby Turner. Affiliations include the Environmental Geodesy Research Group and Climate & Ocean Geoscience Department. Active in high-resolution mantle flow simulations and exascale computing initiatives (e.g., TerraNeo). Awards: Not explicitly listed in provided texts. Grants include ANU Futures Scheme 2.0 and projects on sea-level modelling, geodynamic inversion, and mantle flow reconstructions. Laboratory involvement through G-ADOPT platform development.

• Medical Imaging
• Prostate Cancer
• Magnetic Resonance
• +۲ مورد دیگر

Roger Bourne is an Associate Professor at the University of Sydney specializing in Medical Imaging Science. As Course Director for Medical Imaging Science, he teaches undergraduate and postgraduate medical physics while leading research on magnetic resonance methods for cancer diagnosis. He chairs the University's Prostate Cancer Special Interest Group. Bourne holds a BSc and PhD from the University of Queensland (1983, 1990) and a Graduate Certificate in Education Studies from the University of Sydney (2011). His research spans prostate cancer imaging, diffusion MRI validation, histological-MRI correlation, and radiation dose optimization in breast imaging. Bourne's research develops advanced MRI techniques for characterizing tissue microstructure, particularly in prostate cancer. His work establishes validation frameworks for correlating imaging biomarkers with histological ground truth. Current projects focus on multidimensional MRI, data-driven protocol design, and microstructural modeling of glandular tissues. Recent publications demonstrate Bourne's focus on validating MRI techniques against histological standards, particularly for prostate tissue characterization. His group develops specialized apparatus and computational methods to bridge imaging data and pathological ground truth across cancer types. Bourne leads the Physics and Biomodelling research team and MIOPeG (Image Optimisation and Perception Group). He maintains international collaborations with institutions in Belgium, Finland, the UK, and US focusing on prostate imaging validation and diffusion modeling.

Supriya Supriyaمشاهده پروفایل

مدرس

• Health Informatics
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Supriya Supriya is a Lecturer in the Department of Design and Creative Technology at Torrens University Australia, affiliated with the Centre for Artificial Intelligence Research and Optimisation (AIRO). She holds a Ph.D. in Information and Mathematical Science from Victoria University Melbourne (2020) and master's and bachelor's degrees in Computer Science and Information Technology from Punjab Technical University, India (2014 and 2010). Her research focuses on health informatics, biomedical signal analysis (particularly EEG signals), machine learning, and artificial intelligence. Key areas include癫痫 detection, sleep stage classification, and the application of complex networks and graph-based techniques for medical diagnostics. She has also contributed to deep web data extraction and classification. Supriya has published extensively in reputable journals like IEEE Transactions and conferences, with notable works on EEG-based epilepsy detection and complex network frameworks. Her research aligns with UN Sustainable Development Goals related to education and innovation. She currently supervises PhD students in real-time biomedical signal analysis systems. Her technical expertise includes programming in MATLAB, C++, C#, and Python, and she emphasizes empowering women in STEM fields.

Ammar Alazabمشاهده پروفایل

مدرس

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Dr. Ammar Alazab is a senior Cybersecurity Lecturer at Torrens University Australia, affiliated with the Design and Creative Technology College and the Centre for Artificial Intelligence Research and Optimisation (AIRO). He is actively accepting PhD students in areas such as Enhancing Cyber Threat Detection via Machine Learning and Federated Learning for Secure Cyber Threat Intelligence Sharing. With over 60 research publications and 50+ academic/industry talks, his work bridges AI and cybersecurity, focusing on malware analysis, IoT security, and cybercrime prevention. Education: PhD (Computer Science), Deakin University (2010–2014) Master (Computer Networking), Amman Arab University (2002–2004) Bachelor (Information Technology), Al-Balqa Applied University (1997–2001) Research Interests: Data Science (Hyper-heuristics, Evolutionary Computation), AI for Cybersecurity, IoT Security, Malware Analysis, Blockchain, VR/AR Applications. His work contributes to UN Sustainable Development Goals related to education and innovation. Advising/Grants: Supervises PhD projects in AI-driven cybersecurity and privacy-preserving analytics. No grants explicitly listed in the provided text. Labs/Teams: Leads research within the AIRO Centre, collaborating on topics like prompt attack taxonomy and federated learning defense strategies.

Kamran Shaukatمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Cyber Security
• Health Informatics
• +۵ مورد دیگر

Dr. Kamran Shaukat is a Senior Lecturer at Torrens University Australia's Faculty of Business and Design, where he serves as a Senior Learning Facilitator and researcher in the Centre for Artificial Intelligence Research and Optimisation (AIRO). With over 13 years of academic experience, he specializes in artificial intelligence applications across cybersecurity, medical informatics, and educational data mining domains. His research interests focus on Artificial Intelligence , Cyber Security , Health Informatics , Machine Learning , and Medical Imaging . Key research areas include developing anomaly detection frameworks for Industrial IoT, explainable AI for cardiovascular risk assessment, and deep learning approaches for neurological disorder diagnosis. Analysis of his recent publications reveals a strong trend toward applied AI in healthcare (Alzheimer's detection, skin cancer diagnostics, intradialytic hypotension prediction) and cybersecurity innovation (anomaly detection, malware classification, imbalanced data handling). His work consistently bridges theoretical machine learning with practical implementations in critical infrastructure and medical systems. Award highlights include: Recognition in the top 2% of researchers globally (Stanford University ranking) Dr. Shaukat actively supervises PhD students with current projects focusing on pattern recognition applications in cyber security, medical informatics, medical imaging, and educational data mining. His research contributes to UN Sustainable Development Goals through AI-driven solutions for health and security challenges. He maintains active collaborations across international research networks as evidenced by his co-authorship patterns and citation metrics.

Seyedali Mirjaliliمشاهده پروفایل

استاد

• Optimization
• Swarm Intelligence
• Evolutionary Algorithms
• +۶ مورد دیگر

Seyedali Mirjalili is a Professor of Artificial Intelligence and Director of the Centre for Artificial Intelligence Research and Optimization (AIRO) at Torrens University Australia. He holds distinguished professorships at VSB Technical University of Ostrava, Óbuda University, and De La Salle University, and is an adjunct researcher at Griffith University and Yonsei University. His research focuses on optimization algorithms, swarm intelligence, evolutionary computation, and machine learning. Education: PhD in Computer Science (Griffith University, 2016). Research interests include optimization techniques, swarm intelligence algorithms (e.g., Grey Wolf Optimizer, Whale Optimization Algorithm), machine learning applications, and robust optimization frameworks. He has published over 600 papers with an H-index of 110 and is among the most cited researchers in optimization fields globally. Awards include the Pro-Vice Chancellor Research Special Award (2019) and recognition as a top 1% highly cited researcher (since 2019). He serves as an associate editor for journals like Neurocomputing and Applied Soft Computing. Labs/Teams: Leads the AIRO Centre at Torrens University, focusing on AI-driven solutions for complex optimization problems.