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Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Dr. Andrew Peng is a Lecturer (Research) at the Australian Artificial Intelligence Institute (AAII) within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS), Australia. With dual PhDs from UTS (2015) and Beijing Institute of Technology (2013), he has published 45 peer-reviewed papers across top venues like IEEE ICDM, COLING, and Frontiers in Molecular Biosciences. Education: Dual PhD (2013-2015) from Beijing Institute of Technology and University of Technology Sydney His research focuses on Data Science , Artificial Intelligence , and Healthcare Analytics , addressing challenges in medical data analysis, unstructured clinical text processing, and federated learning frameworks. Recent publications explore: Deep graph clustering for community detection Privacy-preserving medicine shortage detection via social media Time-aware medication recommendation using dynamic treatment regimes Knowledge tracing enhancements for online education Contrastive learning approaches for ICD coding Hypergraph-based sequential diagnosis prediction Dr. Peng has secured over AUD $1M in external research grants and serves as Subject Coordinator for undergraduate/postgraduate courses. He contributes to professional leadership through roles as Web Chair at AJCAI 2021 and ADMA 2021, PC member for major conferences, and reviewer for journals like NeurIPS and AAAI. His work spans collaborations with universities, industry, and government agencies.

• Data Science
• Artificial Intelligence
• Healthcare Analytics
• +3 more

Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)

• Machine Learning
• Artificial Intelligence
• Data Analytics
• +9 more

Minh Hoai Nguyen is a Professor at the School of Computer and Mathematical Sciences and the Australian Institute for Machine Learning (AIML) at the University of Adelaide. Previously, he was a tenured Associate Professor at Stony Brook University (2014-2024) and worked at VinAI Research in Vietnam. He holds a Bachelor of Software Engineering from the University of New South Wales (2006) and a PhD in Robotics from Carnegie Mellon University (2012). His research focuses on computer vision and machine learning, with specialization in human attention modeling, gaze prediction, action recognition, and object counting. Recent work explores applications in educational technology, driver monitoring, and multimodal interaction systems using advanced deep learning approaches. Publication analysis reveals strong emphasis on attention mechanisms across domains, with recent works exploring: (1) Gaze and scanpath prediction using transformer architectures, (2) Few-shot learning for action counting and personalized modeling, (3) Multimodal analysis of cognitive states, and (4) Efficient scene-adaptive vision systems. His research consistently bridges theoretical machine learning with practical applications in human behavior analysis. Scientific Awards: Best Student Paper Award at IEEE CVPR (2012)

• Computer Vision
• Machine Learning
• Human Attention Modeling
• +2 more

Mariano Cabezas Grebol is a researcher affiliated with the Brain and Mind Centre at the University of Sydney . He earned a BSc in Computer Science (2009) and an MSc in Automation, Computation, and Systems (2010) from the University of Girona , followed by a PhD in Technology (2013) focused on atlas-based segmentation of multiple sclerosis lesions . His research bridges neuroimaging and AI models , with specific interests in image synthesis (GANs) , segmentation (cross-sectional/longitudinal) , classification (disease prediction) , uncertainty metrics , and model interpretability . His work has broad implications for Alzheimer's disease , multiple sclerosis , and brain tumor analysis. Scientific Awards : 2020: Understanding Alzheimer's disease with deep learning and neuroimaging grant from the Brain and Mind Centre. International Collaborations : University of Yamagata, Japan (with Dr. Yago in Data Science). VICOROB group, University of Girona, Spain (long-term relationships). His career spans postdoctoral work at VHIR (Hospital Vall d'Hebron) , a Juan de la Cierva-Incorporación fellowship at NIC-VICOROB, and current leadership in developing AI tools for brain image analysis alongside Prof. Michael Barnett in Sydney.

• Neuroimaging
• Artificial Intelligence
• Medical Imaging
• +5 more

Professor J. Joshua Thomas is a distinguished academic at University of Wollongong Malaysia, specializing in intelligent systems and advanced computational techniques. He holds a PhD in Intelligent Systems Techniques from Universiti Sains Malaysia (2015) and a Master's degree in Computer Science from Madurai Kamaraj University, India (1999). Professor Thomas has demonstrated strong leadership in academic administration, having served as Head and Deputy Head of the Department of Computing between 2012 and 2017. His research focuses on intelligent systems and interdisciplinary computational algorithms, with recent work emphasizing Deep Learning, Graph Convolutional Neural Networks (GCNN), Graph Recurrent Neural Networks (GRNN), Hyper-Graph Attention Networks, and Quantum Machine Learning. Professor Thomas's projects span diverse applications including end-to-end steering learning systems, algorithm design in drug discovery, and advanced data analytics for electricity consumption and carbon price forecasting. Professor Thomas maintains an impressive publication record with over 50 peer-reviewed publications and 12 books with publishers including Wiley, Elsevier, and IGI Global. His research is supported by multiple active grants at institutional, national, and international levels, including projects on cancer survivor well-being and AI bias in smart cities. Best Paper Award at SCI 2025 Oracle Cloud Infrastructure 2024 Generative AI Certified Professional 2022 Global Staff Awards (UOWGE) - Excellence In Research Winner V-MIIEX2021: COVIDNet - GOLD AWARD Fundamental Research Grant Scheme (FRGS) recipient (2019) As an active researcher and educator, Professor Thomas serves as Principal Investigator on multiple grants, supervises PhD and Master's students, and regularly delivers keynote addresses at international conferences. His collaborative spirit is evident through partnerships with institutions in India, USA, China, and Germany, fostering knowledge exchange and innovation across disciplines.

• Intelligent Systems
• Deep Learning
• Graph Neural Networks
• +5 more

Dr. Lee Sue Han serves as Senior Lecturer and Head of Department for Information and Communication Technologies at Swinburne University of Technology Sarawak's Faculty of Engineering, Computing and Science. She joined Swinburne in April 2020 following post-doctoral research with Pl@ntNet (CIRAD and INRIA) in France. Her academic credentials include a BEng (Hons) in Electronic Engineering from Multimedia University (2011), MSc in Electrical and Electronic Engineering from Shinshu University, Japan (2014), and PhD in Computer Science from University of Malaya (2018). Dr. Lee's research bridges artificial intelligence and plant biology, focusing on computer vision, machine learning, and deep learning applications for plant disease identification, cross-domain classification, and environmental monitoring. Her work explores how deep learning models interpret botanical features and align with human perception in disease diagnosis, with significant contributions to automated plant health systems. Recent publications (2021-2022) demonstrate advanced work in multi-task learning for disease identification, global-scale plant recognition, and interpretable AI models. Her research consistently appears in top venues including Frontiers in Plant Science, IEEE Transactions, and Pattern Recognition. Her accolades include the 2022 Outstanding Student Feedback on Teaching award, 2022 Swinburne Research Success Award, and Best Paper Awards in 2020 and 2018. She actively secures research funding as PI for FRGS 2021 and co-PI for FRGS 2022 and SDEC 2021 projects. Dr. Lee supervises research in generative models, sign language production, drug discovery, animal re-identification, and plant disease modeling. She maintains international collaborations initiated during her France-based post-doctoral work and participates in global challenges like PlantCLEF.

• Computer vision
• Artificial intelligence
• Machine Learning
• +2 more

Dr. Bin Liang is a Senior Lecturer at the University of Technology Sydney (UTS), working within the Faculty of Engineering and Information Technology and the Data Science Institute. He joined UTS in December 2018 as a Lecturer and was promoted to Senior Lecturer in July 2022, following his postdoctoral fellowship at Data61 (CSIRO). PhD in Computer Vision, Pattern Recognition, and Machine Learning from Charles Sturt University, Australia (2012-2015) Masters in Computer Science from Taiyuan University of Technology, China (2009-2012) BEng in Computer Science from Taiyuan University of Technology, China (2005-2009) Dr. Liang's research spans data mining, machine learning, computer vision, pattern recognition, and survival analysis, with a strong focus on practical applications in critical infrastructure. His work develops sophisticated machine learning frameworks for anomaly detection, failure prediction, and optimization in water infrastructure, flood mapping, and real estate appraisal. He has pioneered approaches that integrate graph neural networks with temporal analysis for pipe failure prediction and has made significant contributions to water quality optimization through data-driven methods. His research consistently bridges theoretical advancements with practical implementation in industry settings. His recent publications demonstrate a clear trajectory toward increasingly sophisticated models that capture complex temporal and spatial relationships in infrastructure data. There's a notable emphasis on multimodal learning approaches and domain generalization techniques that enhance model robustness across diverse application scenarios. His work on anomaly detection frameworks like MLAD shows innovation in grouping sensors by temporal characteristics for more accurate monitoring. 2022 – AWA R&D Excellence Award (NSW) 2022 – UTS Medal for Research Impact 2022 – Finalist for 2021-22 AWA Young Water Professional of the Year Award (NSW) 2020 – Finalist for the Best Paper Award of the ICARCV 2020 2019 – Industrial & Primary Industries Merit at the Victorian iAwards 2018 – Australian Museum Eureka Prize for Excellence in Data Science Dr. Liang actively supervises Masters Research and PhD students, focusing on data science applications for infrastructure management. His research has been supported through industry partnerships with water utilities and other infrastructure organizations, translating academic research into practical solutions that reduce water loss, improve infrastructure reliability, and enhance environmental sustainability. His work on pipe failure prediction and leak detection has directly contributed to operational improvements in water distribution networks. As a core member of the Data Science Institute at UTS, Dr. Liang collaborates with interdisciplinary teams working on data-driven solutions for urban infrastructure challenges. His research group focuses on developing scalable machine learning models that can be deployed in real-world settings, often working directly with industry partners to validate and implement their approaches. This industry-academia collaboration ensures that his research remains grounded in practical challenges while pushing the boundaries of data science methodology.

• Data Mining
• Machine Learning
• Computer Vision
• +5 more

Dr. Keshav Sood is a Senior Lecturer at Deakin University's School of Information Technology, where he leads research in cybersecurity, AI, and next-generation networks. His affiliations include roles as Graduate Research Coordinator and South Asia Country Coordinator. He completed his PhD at Deakin University and a post-doctoral fellowship at the University of Newcastle. Research Interests: Dr. Sood focuses on securing distributed systems, with emphasis on: Federated learning for intrusion detection in IoT/5G networks Biometric privacy in immersive technologies (VR/AR) RF fingerprinting for IoT device authentication Quantum-resistant software-defined networks Adversarial robustness in voice authentication systems His publications consistently explore AI-driven security frameworks, with recent work addressing data sparsity in IoT sensors, cross-domain IIoT authentication, and phishing mitigation using large language models. Awards: Professor of IT Award (2016) IEEE TNSE Excellent Reviewer (2023) Deakin HDR Supervision Award (2023) Course Team Award for Industry Certification Alignment (2021) Supervision & Grants: Dr. Sood currently advises 8 graduate researchers and has secured $655,313 in competitive funding. Key projects include: Smart Farming Cyber Resilience (DFAT Maitri Grant) Secure Access for Critical Infrastructure (Cyber CRC) IoT Data Integrity for Defense Systems (Australian Defence) He leads the Deakin Cyber Research and Innovation Centre, focusing on scalable security solutions for industry partners.

• Cybersecurity and Privacy
• Artificial Intelligence
• Distributed Computing
• +6 more

Dr Elizabeth Englezos is a Lecturer at Griffith Law School, Griffith University, appointed since February 2024. She serves as a member of the QLS Advisory Group on Generative AI in Legal Education and previously contributed to the Law Futures Centre (2023-2024). Her academic appointments include editorial roles as Managing Editor of Griffith Law Review (2025-present) and Copy-editor for the Journal of Cross-disciplinary Research in Computational Law (2024-present). Her educational background features: Doctor of Philosophy (PhD), Griffith University (2022), thesis: '#Hyperconnection: Law and the digital influence on individual identity' Bachelor of Laws (First Class Honours), Griffith University (2019) Graduate Diploma of Legal Practice, Queensland University of Technology Dr Englezos' research examines law's intersection with digital technologies, focusing on digital identity harms, algorithmic decision-making, and privacy regulation. Her work analyzes how digital environments impact individual autonomy through platforms, misinformation ecosystems, and AI systems. Key contributions address platform liability frameworks, democratic challenges in digital public spaces, and regulatory responses to emerging technologies. She employs interdisciplinary methodologies combining legal theory, semiotic analysis, and policy evaluation to develop practical governance solutions for contemporary digital dilemmas. Her publication trajectory (15 outputs 2018-2025) reveals increasing focus on AI governance and platform accountability, with recent works examining algorithmic policing, biodiversity law in pandemic contexts, and generative AI implications. The research consistently bridges theoretical analysis with practical regulatory applications across jurisdictions. Dr Englezos actively translates research into pedagogy through her 'Law and Digital Media' elective (launched 2022) and Contracts 1 teaching. Her professional engagement includes policy submissions to government consultations and editorial leadership. Current activities center on generative AI's impact on legal education through her QLS Advisory Group membership, positioning her at the forefront of emerging digital law challenges.

• Digital Media Law
• Technology Law
• Data Privacy
• +5 more

Dr. Gary Clark is a Lecturer in Teaching & Research at La Trobe University's Department of Ecological Plant and Animal Sciences. His work focuses on sustainable soil management for agricultural resilience, particularly addressing constraints in Australian soils through biological methods like multi-species pastures, cover crops, and organic amendments. Education BSc(Hons) from La Trobe University M Agriculture from the University of Melbourne PhD from La Trobe University Graduate Diploma in Higher Education from La Trobe University Research Focus Clark's research explores soil health improvement via biological interventions, including: Sustainable agricultural practices for climate resilience Soil structure enhancement through plant-based solutions Organic amendment efficacy in constrained soils Ecosystem responses to fire and land management His publications demonstrate consistent focus on soil-plant-microbe interactions across diverse ecosystems. Professional Activities President of Soil Science Australia - Victorian Branch (since 2022) Teaching & Supervision He coordinates courses including AGR2ILS (Introduction to Land and Soil), AGR3LS (Land and Soil Management), and AGR2WIL (Work Integrated Learning). Available for Masters/PhD supervision and industry collaborations. Funded Research Next-generation weed management (2025-2030) Maize Field Trial with AgBioEn Pty Ltd (2020-2023)

• Sustainable Agriculture
• Soil Science
• Cover Cropping
• +5 more

Christopher Drovandi is a Professor at Queensland University of Technology and a Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research spans statistical methodology development and applications across diverse fields including ecology, biomedicine, and environmental science. Dr. Drovandi's primary research interests focus on advancing Bayesian statistical methods, particularly in the areas of: Approximate Bayesian Computation (ABC) and likelihood-free inference Synthetic likelihood methods Sequential Monte Carlo algorithms Bayesian experimental design Statistical computing for complex models His recent work demonstrates a strong emphasis on addressing model misspecification in Bayesian inference, developing robust computational methods for intractable likelihoods, and applying advanced statistical techniques to solve real-world problems in ecology, biomedicine, and environmental science. Drovandi has made significant contributions to both the theoretical foundations and practical applications of modern Bayesian statistics. Dr. Drovandi has received research funding through multiple Australian Research Council grants and collaborates extensively with researchers across disciplines. His work has been published in top-tier statistical and interdisciplinary journals including the Journal of the American Statistical Association, Bayesian Analysis, and PLOS Computational Biology.

• Bayesian Statistics
• Approximate Bayesian Computation
• Statistical Modeling
• +6 more

Christy Jie Liang is an Associate Professor at the School of Computer Science, University of Technology Sydney (UTS), where she leads the Data Visualisation Research Lab in the Visualisation Institute. With extensive experience in both academic and industry settings, including appointments at IBM and Peking University, she has established herself as a leading researcher in data visualization and visual analytics. Dr. Liang earned her PhD in Data Visual Analytics from UTS, where she was awarded the University Medal with First Class Honours. Her educational background includes a Bachelor of Information Technology (First Class Honours) also from UTS. Professor Liang's research focuses on data visualization and visual analytics, with particular emphasis on information visualization, narrative visualization, and the application of these techniques to real-world problems. Her work spans multiple domains including finance, food safety, biomedical applications, smart cities, and social media. She has developed novel visualization techniques and owns five intellectual properties in this field. Her recent publications demonstrate a clear trajectory toward more sophisticated visualization techniques that integrate machine learning, with increasing focus on narrative visualization, user engagement across demographics, and practical applications in domains such as public health and education. The interdisciplinary nature of her work is evident in collaborations across computer science, behavioral science, and domain-specific applications. Dr. Liang has received significant recognition for her work, including: University Medal with First Class Honours from UTS Capital Markets CRC Honours scholarship Australian Postgraduate Awards As an educator, Professor Liang coordinates core subjects for Bachelor of Information Technology, Bachelor of Computer Science with Honours, Master of Interaction Design, and Master of Business Analytics programs. She has recently developed enterprise learning courses including short courses and micro-credentials in data visualization education. Her leadership extends to service roles as associate editor for JVLC and Journal Visual Informatics, program committee member for numerous conferences, and advisory board member for the Australian Computer Society and Peking University Medical Visualization Centre. Professor Liang leads the Data Visualisation Research Lab, which focuses on developing innovative visualization techniques and applying them to real-world problems. The lab maintains strong industry connections, with collaborations spanning government agencies, academic institutions, and commercial enterprises across multiple continents.

• Data visualisation
• Visual Analytics
• Information Visualisation
• +4 more