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Basem SuleimanView profile

Senior Lecturer

• AI-driven Software Services
• Applied Artificial Intelligence (GenAI/LLM, Machine Learning)
• Education Technologies (Personalized Learning)
• +4 more

Dr. Basem Suleiman is an Adjunct Senior Lecturer in Advanced Computing at the University of Sydney and Deputy Director of the Software Engineering Program at UNSW Sydney. He holds a Ph.D. in Computer Science from UNSW (2012) and has over a decade of experience in academia and industry, including roles at SAP, CSIRO’s Data61, and leading universities. Education: Ph.D. in Computer Science, UNSW Sydney (2012) Graduate Certificate in Research Commercialization & Management, UNSW Sydney Master of Computing (Software Engineering), Western Sydney University Research Interests: AI-driven Software Services: Federated Learning, Recommender Systems, Human-Machine Interaction Applied AI: GenAI/LLMs, Machine Learning, Deep Learning for FinTech, Healthcare, and Education Cloud Computing: Elasticity, Workload Management, Privacy-Preserving Techniques Key Contributions: Developed AI models for structural health monitoring, personalized learning systems, and anomaly detection Secured research grants and published 80+ papers in venues like ACM Transactions, IEEE Conferences, and IJCAI Supervised 15+ PhD/MPhil students and led industry partnerships with SAP, CSIRO, and global tech firms Awards & Recognition: Runner-up Best Paper Award (2023) Dean’s Commendation for Teaching Excellence Invited Keynote Speaker (2022 ICoSEIT) Engagement: Leads industry collaborations as UNSW’s Industry Capstone Liaison Officer, fostering partnerships for student projects and curriculum innovation.

• Bayesian Methodology
• Statistical Machine Learning
• Variational Bayes
• +3 more

Dr. Minh-Ngoc Tran is an Associate Professor in Business Analytics at the University of Sydney Business School and an Associate Investigator in the ARC Centre of Excellence for Mathematical and Statistical Frontiers. His research develops Bayesian and machine learning methods for complex models with big data. His methodological innovations include quantum-enhanced variational Bayes, manifold optimization techniques, and subsampling MCMC algorithms. Current research focuses on time series modeling for financial markets, quantum computing applications in statistics, and variational inference for models with intractable likelihoods. Dr. Tran teaches quantitative methods, data mining, and predictive analytics. He currently supervises PhD students in deep learning for financial time series, explainable AI, and quantum machine learning. Recent research grants support quantum computation in business analytics and deep learning-based financial forecasting. His work has been recognized through awards including the University of Sydney Business School Emerging Scholar Research Fellowship and designation as Australia's top researcher in Probability and Statistics with Applications in 2021.

Priyanka SinghView profile

Lecturer

• Cyber Security
• Digital Forensics
• Privacy and Security Aspects
• +5 more

Dr. Priyanka Singh is a Lecturer in Cyber Security at the School of Electrical Engineering and Computer Science, University of Queensland. She holds a PhD in Image Forensics and has conducted postdoctoral research at SUNY Albany and Dartmouth College, collaborating with Prof. Hany Farid. Her research focuses on Cyber Security, Digital Forensics, Privacy, Homomorphic Encryption, and Cloud Computing applications. Key contributions include real-time child pornography detection and privacy-preserving frameworks for digital authentication. Education: PhD in Image Forensics (university unspecified), postdoctoral work at SUNY Albany and Dartmouth College. Notable collaborations include a talk invitation at MIT by Prof. Ramesh Raskar. Research Interests: Cyber Security mechanisms, Digital Forensics techniques, Privacy-Preserving algorithms, Homomorphic Encryption protocols, and Cloud Security solutions. Recent Articles: Focus on AI ethics (e.g., FakeFaceDiscriminator), privacy-preserving biometrics (e.g., IRIS-SAFE), and secure anomaly detection (e.g., SADHE). Her work spans 42 conference papers and 22 journal articles, addressing challenges like encrypted video tampering detection and federated learning resilience. She advocates for technology's societal impact through explainable AI and ethical AI use cases.

Gayan Chaminda LankeshwaraView profile

Research Fellow

• Grid Integration of Distributed Energy Resources
• Active Distribution Network Management
• Machine Learning Applications in Power Systems
• +3 more

Gayan Chaminda Lankeshwara is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland (Australia). He holds a B.Sc.Eng. (Hons.) and M.Sc.Eng. in Electrical and Electronic Engineering from the University of Peradeniya, Sri Lanka (2016 and 2021), and a Ph.D. in Electrical Engineering from The University of Queensland (2023). His research focuses on grid integration of distributed energy resources, active distribution network management, and machine learning applications in power systems. His work emphasizes dynamic operating envelopes frameworks, demand response mechanisms, and low-voltage distribution networks optimization. Key contributions include developing control schemes for thermostatically controllable loads, peer-to-peer energy trading models, and hierarchical control strategies for residential loads under uncertainties. His research bridges theoretical advancements with practical applications in smart grid technologies. Lankeshwara has contributed to over 11 publications, including 8 journal articles and 3 conference papers, focusing on renewable energy integration, demand response algorithms, and network-aware control systems. His doctoral thesis (2023) explored 'Network-aware demand response in the presence of uncertainties.' He collaborates with the Power, Energy and Control Engineering research group at UQ, contributing to projects that enhance grid resilience and efficiency through advanced control methodologies and data-driven approaches.

Hanna KurniawatiView profile

Professor

• Robotics
• Decision-Making Under Uncertainty
• Motion Planning
• +4 more

Hanna Kurniawati is a Professor at the ANU School of Computing and holds the SmartSat CRC Professorial Chair for System Autonomy, Intelligence, and Decision-Making. She leads the ANU Node and Planning & Control theme of the ARIAM Hub, and founded the Robot Decision Making group. Her research focuses on scalable algorithms for decision-making under uncertainty, particularly in robotics and autonomous systems. She has received prestigious awards, including the RSS 2021 Test of Time Award and multiple best-paper recognitions. She is an active conference organizer and editorial board member for journals like IEEE Transactions on Robotics. Education: BSc Computer Science, University of Indonesia PhD Computer Science (Robot Motion Planning), National University of Singapore (2008) Research Interests: Planning under uncertainty and POMDP frameworks Robot motion planning and computational geometry Integration of planning and reinforcement learning Autonomous system assurance and safety Service and Leadership: Former President of the Australian Robotics and Automation Association (2019–2020) Keynote speaker at ICRA 2025, IROS 2018, and ICAPS 2025 Program Co-Chair for ICRA 2022 and Editor roles for IEEE TRO and RA-L Teaching: Advanced Topics in AI (COMP4620/8620) Advanced Topics in Machine Learning (COMP4680/8650) Algorithms (COMP3600/6466)

Shyh Wei TengView profile

Associate Professor

• Image Processing
• Machine Learning
• Bioinformatics
• +3 more

Assoc Prof Shyh Wei Teng is an Associate Professor and Deputy Dean at Federation University Australia's Institute of Innovation, Science and Sustainability (IISS). He holds a Bachelor of Computing (First-class Honours) and PhD in Information Technology from Monash University (2004), and a Graduate Certificate in Higher Education (2008). His research focuses on image processing, machine learning, bioinformatics, and computer vision, with notable contributions to LiDAR-based building extraction, SIFT algorithm optimization, and multimodal image registration. He has secured significant grants, including an Australian Research Council (ARC) Discovery Project (2013). Prof Teng has supervised numerous PhD and master’s students, contributing to advancements in image retrieval, clustering algorithms, and biomedical applications. His work has been published in top-tier journals like Pattern Recognition and Neurocomputing , and he actively reviews for IEEE conferences. He taught courses in database systems, project management, and systems analysis, receiving teaching and community engagement nominations. Research interests span computational techniques for medical imaging, contour-based corner detection, and machine learning applications in remote sensing. His labs and collaborations include projects on augmented reality for education and ecological data analysis.

Dianne CookView profile

Professor

• Data Science
• Data Visualization
• Exploratory Data Analysis
• +5 more

Prof. Dianne Cook is a Professor of Business Analytics at Monash University's Department of Econometrics and Business Statistics, affiliated with Impact Labs. A Fellow of the American Statistical Association and Editor of the Journal of Computational and Graphical Statistics, her research focuses on data visualization, exploratory data analysis, and statistical graphics. Her work bridges statistical inference and visualization, leveraging crowd-sourcing and eye-tracking to validate visual discoveries. Major contributions include software tools like ggobi and cranvas, and methodologies like the 'tour' for high-dimensional data exploration. Current projects include spatio-temporal bushfire visualization and Melbourne pedestrian data analysis. Key collaborations span physics data visualization and multidimensional analysis. Awards include ASA Fellowship. Projects include Accessibility Enhancements for the R Journal and Multivariate Spatio-Temporal Visualization for bushfire risk. Teaching and advising emphasize open-source software and reproducible research. She leads RiskLab projects on econometrics and explores applications in climate change, education, and health. Her work contributes to UN SDGs like Sustainable Cities (Goal 11) and Quality Education (Goal 4).

• Emergency Nursing
• Public Health
• Clinical Leadership
• +2 more

Lisa Kuhn is an Associate Professor in the School of Nursing, Midwifery and Paramedicine at Nebraska Methodist College. Her research focuses on emergency nursing practices, public health responses to conflicts and disasters, cardiovascular care optimization, and occupational health in healthcare settings. She has led numerous studies on clinical protocols in emergency departments and conflict zones. Her research interests span emergency medicine, clinical leadership development, occupational safety for healthcare workers, and innovative approaches to nursing education. She investigates trauma care systems, disaster preparedness, and healthcare worker safety in high-risk environments. Recent publications demonstrate a focus on evidence-based emergency care protocols, conflict medicine competencies, and healthcare system improvements. Her work frequently addresses clinical decision-making in cardiac emergencies, occupational hazards in nursing, and innovative training methodologies. Professional contributions include development of clinical guidelines for emergency departments and conflict zones. She actively researches healthcare delivery optimization through technological innovations and protocol standardization. Her investigations into nursing competencies in armed conflict contexts have established frameworks for emergency response training and clinical practice in resource-constrained environments.

Samuel BecherView profile

Professor

• Economic analysis of law
• Behavioral analysis of law
• Law and Happiness
• +7 more

Professor Samuel Becher is affiliated with the School of Accounting and Commercial Law at Victoria University of Wellington, New Zealand. A graduate of Yale Law School (LLM, JSD) and Tel Aviv University (LLB), he specializes in interdisciplinary legal scholarship at the intersection of law, economics, psychology, and technology. Key research themes include AI-transformed legal systems, consumer law, contract theory, and climate-related disclosures Active in public engagement, he served on Israel's Form Contract Tribunal and led the Dispute Resolution Institute at 'Public Trust' Current projects funded by the Gama Foundation, Borrin Foundation, and Earthquake Commission His scholarship on AI Legal Agents and Smart Readers has been published in top journals like the Journal of Legal Studies and George Washington Law Review. While he has received multiple teaching awards , his recent work focuses on machine-readable legal systems and the ethical implications of DNA testing. Professor Becher offers supervision for Masters and PhD research in commercial law and legal technology domains.

Shahab AbdullaView profile

Associate Professor

• Artificial Intelligence
• Bioinformatics
• Control Systems
• +9 more

Dr. Shahab Abdulla is an Associate Professor at the University of Southern Queensland (UniSQ), affiliated with UniSQ College. His interdisciplinary research focuses on Artificial Intelligence, Bioinformatics, Control Systems, and Signal Processing. He holds a PhD from USQ and has over 25 publications in Q1 journals, primarily in Computer Science and Engineering domains. He leads the Advanced Data Analytics Modelling Research Simulation Group and supervises numerous PhD/Master’s students. Education: BSc in Mathematics from University of Technology (Baghdad, 1992) MSc in Mathematics from University of Technology (Baghdad, 1996) PhD in Engineering from University of Southern Queensland (2013) Research Interests: Dr. Abdulla specializes in AI models for healthcare, energy, and biomedical applications. His work includes deep learning, neural networks, and signal processing techniques. He collaborates globally with researchers in the Middle East, Europe, and Asia, delivering over 30 seminars. His research has been cited over 200 times in the last three years. Grants & Awards: While specific awards are not listed, his leadership has been recognized through institutional and professional acknowledgments. He has secured grants for projects like AI-driven coastal erosion modeling and quantum-resistant cryptography. Supervision: Currently supervising 5 PhD/Master’s students and has examined numerous theses. His supervision spans topics like AI in healthcare, cybersecurity, and sustainable agriculture. Labs & Teams: Leads the Advanced Data Analytics Modelling Research Group, focusing on interdisciplinary AI applications. Collaborates with industry partners on projects like smart solar trackers and flood risk prediction systems.

• Supply Chain Management
• Logistics Optimization
• Sustainability
• +3 more

Mahla Babagolzadeh is a Lecturer (Casual Employment) at the University of Southern Queensland's School of Business. Her research focuses on sustainable supply chain management, logistics optimization, and operations research, particularly addressing challenges in perishable goods distribution, carbon tax policy impacts, and regional development. Education: Bachelor of Industrial Engineering (BIndEng) from Sadjad University of Technology Master of Science (MSc) from Ferdowsi University of Mashhad Doctor of Philosophy (PhD) from the University of Southern Queensland Research Interests: Dr. Babagolzadeh explores sustainable logistics systems, cold chain management under uncertainty, and innovative supply chain models for perishable products. Her work integrates environmental economics with operational strategies, emphasizing carbon tax regulation and government subsidy frameworks to enhance regional economic resilience. Publications Trends: Her recent articles (2016–2024) highlight contributions to multimodal transportation networks, vehicle routing optimization, and disaster relief logistics. She frequently employs hybrid algorithms and stochastic modeling to address real-world complexities in supply chain planning and reliability estimation. Advising & Grants: No formal advisees or grant details are listed in the provided data. Labs/Teams: No specific labs or collaborative teams are mentioned in the profile.

Jonathan GreenView profile

Associate Professor

• Higher Education
• Language and Literacies
• English for Academic Purposes
• +3 more

Jonathan Green is an Associate Professor and Deputy Head of School (Education) at UniSQ College (Pathways), University of Southern Queensland. He holds qualifications including a PhD from USQ and has over 15 years of academic experience spanning Australia and Thailand. Bachelor of Arts (Witwatersrand) Certificate in Advanced Graduate Studies (New England) Master of Education (USQ) Doctor of Philosophy (USQ) His research focuses on higher education access, language pedagogy, and cross-cultural education. Key areas include academic literacy development, transfer of learning in Thai education systems, and policy compliance in TQM frameworks. His work bridges educational psychology with practical applications like EEG signal analysis for medical diagnostics, showcasing interdisciplinary collaboration. He has contributed to pathway programs at regional universities and authored over two dozen scholarly articles since 2008. Professional memberships include the National Association of Enabling Educators of Australia (Executive Committee) and Senior Fellow of the Higher Education Academy.

Cheryl McCarthyView profile

Associate Professor

• Agricultural Engineering
• Machine Vision
• Robotics
• +5 more

Dr. Cheryl McCarthy is an Associate Professor in Mechatronic Engineering and Senior Research Fellow at the University of Southern Queensland, focusing on automation and machine vision technologies in agriculture. She has been a full-time researcher since completing her PhD in 2009, with expertise in machine vision, robotics, and precision agriculture. Education: BEng (2005) and PhD (2004) in Mechatronic Engineering from the University of Southern Queensland. Research interests include crop disease detection (e.g., wheat root rot using NIR spectroscopy and machine learning), automation in farming operations, and robotic systems for precision agriculture. Her work spans orchard mapping, UAV-based irrigation monitoring, and livestock welfare assessment via machine vision. She has led projects on weed detection, fruit load estimation, and robotic navigation in agricultural environments. Key contributions include developing the 'MangoYOLO' deep learning model for fruit detection and advancing non-destructive crop sensing technologies. Her research integrates robotics, computer vision, and environmental sensing to address challenges in sustainable agriculture. Current and completed supervisions include projects on machine learning applications in meat processing, precision agriculture, and robotic localization in orchards. Major grants include farm trials for chicken welfare monitoring (2023), precision weed detection systems (2016), and harvester loss assessment (2019). Labs/Teams: Works within the Centre for Agricultural Engineering, collaborating on projects like space agriculture sensing and crop disease diagnostics.

• Surveying
• LiDAR Technology
• Cadastral Systems
• +4 more

Glenn Campbell is an Associate Professor (Surveying) at the University of Southern Queensland's School of Surveying and Built Environment. His research spans spatial data law, LiDAR applications, cadastral systems, and remote sensing of water resources. Campbell employs advanced techniques including machine learning for topographic feature extraction and matrix inversion methods for water quality assessment. Research Focus: Geospatial law and indigenous land boundaries LiDAR processing for urban feature extraction Water quality remote sensing techniques Cadastral system modernization His publications demonstrate consistent focus on measurement accuracy, spatial data governance, and advanced geospatial analysis techniques. Recent work shows increasing emphasis on digital copyright issues in spatial data and machine learning applications for LiDAR processing. Awards & Recognition: No major scientific awards reported. Student Advising: No advisee information available.

Frits de NijsView profile

Research Fellow

• Artificial Intelligence
• Renewable Energy Systems
• Reinforcement Learning
• +3 more

Frits de Nijs is a Research Fellow in the Department of Data Science & AI at Monash University. His work focuses on advancing artificial intelligence techniques for energy systems optimization, multi-agent decision-making, and sustainable infrastructure. He has contributed to projects such as the Human-in-the-loop Microgrid Project and RACE for 2030 CRC, addressing challenges in renewable energy integration and smart grid management. Key research areas include reinforcement learning applications for energy curtailment, HVAC control systems, and resource-constrained multi-agent planning. His projects often bridge AI methodologies with real-world energy challenges, aiming to enhance grid reliability, carbon efficiency, and fair resource allocation. Notable collaborations involve researchers in machine learning, sustainable energy, and building automation. He has actively participated in 6 major research projects since 2020, including initiatives on AI-ready buildings and probabilistic multi-agent learning environments. His publications span conferences like AAAI, NeurIPS, and IEEE Power & Energy Society events, emphasizing peer-reviewed contributions to energy-AI intersections.