جست‌وجوی استاد و پژوهشگر

Badal Pokharelمشاهده پروفایل

پژوهشگر ارشد

• Geospatial Engineering
• Disaster Risk Reduction and Management
• Climate Change
• +۴ مورد دیگر

Dr. Badal Pokharel is a Research Associate 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), Faculty of Engineering. She holds a PhD in Civil and Environmental Engineering from UNSW (2023) and an M.Sc. in Engineering Geology from Tri-Chandra Multiple Campus, Tribhuvan University, Nepal (2018), where she was the university topper. Her research focuses on the assessment of natural and human-induced disasters, particularly earthquake-triggered landslides and climate change-induced floods, using advanced geospatial approaches. Dr. Pokharel has extensive expertise in Geographic Information Systems (GIS), remote sensing (RS), and implementing geospatial systems for geohazards, agriculture, climate change, land administration, and geomorphology projects. Her methodological expertise integrates machine learning techniques with traditional geospatial methods, showing particular focus on the Himalayan region, especially Nepal. Dr. Pokharel's publications demonstrate strong expertise in landslide susceptibility mapping, earthquake-triggered landslides, and geospatial analysis of natural disasters. Her recent work includes innovative approaches using random forest methods, Persistent Scatterer Interferometry, and slope-unit-based assessments for disaster risk management, with a geographical focus on Central Nepal and the Himalayan region. Early Career Academic Social Impact Award 2023, UNSW Early Career Network (ECAN), Sydney Australia Best Talent Award, 2021 Young Generation Forum/Young Professional Forum hosted by the Korean Academy of Scientists and Engineers in Australasia University topper for Master of Science in Engineering Geology, Tri-Chandra Multiple Campus, 2018 Dr. Pokharel has secured multiple research grants including UNSW Seed funding (AUD 25000), Global Partnership Project Grant from Virginia Tech University (AUD 7200), HPC-Europa3 Transnational Access Programme research grant (AUD 3630), and PhD Research Support Grant from UNSW (AUD 3000). She is passionate about interdisciplinary and transdisciplinary research that contributes towards disaster risk reduction and management, bridging engineering, geoscience, and data science to develop practical solutions for natural disaster prediction and management.

Ong Huey Fangمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Data Mining
• Bioinformatics
• +۷ مورد دیگر

Dr. Ong Huey Fang is a Senior Lecturer in the School of Information Technology at Monash University Malaysia since 2019. She holds a PhD in Intelligent Computing from Universiti Putra Malaysia, with earlier degrees from Universiti Teknologi Malaysia. Her research focuses on artificial intelligence applications in bioinformatics (e.g., cancer biomarker discovery), machine learning (transfer learning for Malaysian English), and computer vision (micro-expression analysis). She has led projects like the 2022 Deep Associative Classification initiative. Professional memberships include IEEE, ACM, and AWS certification. Current work integrates multi-omics data analysis and causal graph modeling to address biases in AI systems. Education: BSc (UTM), MSc & PhD (UPM) Professional Certifications: AWS Cloud Practitioner Research spans cancer biomarkers via omics data, NLP for low-resource languages, and VR-based educational tools. Recent projects include causal analysis of video micro-expression bias and blockchain-enhanced healthcare systems. She actively collaborates internationally on topics like biomedical event extraction and supply chain digitalization.

Emma-Lee Steindlمشاهده پروفایل

مدرس

• STEM education
• Trauma-aware education
• Psychology
• +۱۲ مورد دیگر

Dr Emma-Lee Steindl serves as a Lecturer in Education at the University of the Sunshine Coast's School of Education and Tertiary Access. She maintains an active research profile while contributing to undergraduate and postgraduate teaching programs. Her academic journey includes significant experience across multiple educational contexts including on-campus instruction, distance education, special education settings, and English language preparatory schools. Her research interests span multiple interconnected domains: STEM education with particular focus on mathematics and science teaching methodologies Trauma-aware and trauma-informed educational practices Expressive therapies including music therapy applications in educational settings Educational and developmental psychology perspectives Cultural considerations in inclusive educational environments Barriers and facilitators to accessing education and allied health services Dr Steindl's recent scholarly output reveals an evolving trajectory from classroom practice to academic research, with increasing emphasis on interdisciplinary connections between engineering education research, trauma-informed practices, and psychological wellbeing. Her 2025 systematic literature review on engineering education researcher development demonstrates methodological sophistication while her 2024 Declaration for a Trauma-Informed Education Workforce in Australia shows national policy influence. Her notable awards include: Associate Fellow (Indigenous Knowledges) of the Higher Education Academy (2024) Highly Commended Tutor Award from Queensland University of Technology (2024) Multiple Staff Recognition Awards for Individual and Team Impact (2023) Vice Chancellor's Award for Excellence in Innovation and Creative Practice (2019) Dr Steindl has secured research funding including the 2023-2025 Royal Society of Chemistry grant "Balancing Learning and Life to Become a Chemist" which investigates external responsibilities shaping student success inequity. She has partnered with academics, industry professionals, and volunteers to implement learning programs such as Room 17 Goes Large and STEM-antics, demonstrating commitment to both educational innovation and student wellbeing. Her professional memberships across counseling, psychology, and education associations reflect the interdisciplinary nature of her work in educational support systems.

Shuang Aoمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Reinforcement Learning
• Curriculum Learning
• +۴ مورد دیگر

Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Dr. Alan Blair is a Senior Lecturer at the School of Computer Science and Engineering, University of New South Wales. His research spans artificial intelligence, machine learning, and neural networks, with applications in medical imaging, evolutionary computation, and cybersecurity. Key research areas: Artificial Intelligence, Machine Learning, Neural Networks, Evolutionary Computation, Medical Imaging, Computational Linguistics Recent publications focus on reinforcement learning for medical diagnosis, dialogue act tagging, and phishing detection using feature-free tools. Collaborations include work with Sowmya A, Iyer S, and Dawes L on 3D organ localization and fracture detection in CT scans. Blair's work integrates hybrid neural-logical reasoning models for visual question answering and hierarchical evolutionary algorithms for optimization tasks. He has contributed to adversarial image generation and game theory applications.

Helia Farhoodمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Dr. Helia Farhood is an Honorary Senior Research Fellow at the School of Computing, Macquarie University, specializing in Artificial Intelligence, Machine Learning, and Image Processing with applications in educational technology and object recognition. Her academic qualifications include a PhD in Computer Systems and Artificial Intelligence from the University of Technology Sydney (awarded November 2021) and a Master's degree in Computer-AI from Amirkabir University of Technology (Tehran Polytechnic, awarded September 2013). Dr. Farhood's research spans interdisciplinary AI applications, with significant contributions in student outcome prediction using generative adversarial networks, explainable AI through LIME heatmaps, and image-based storytelling systems. Her work integrates machine learning with educational data mining to enhance creativity assessment and learning analytics, while maintaining strong technical focus on 3D reconstruction and object recognition. Analysis of her 16 publications (2020-2025) reveals three dominant research trajectories: (1) AI-driven educational analytics for student performance prediction, (2) advanced image processing techniques for object recognition and 3D reconstruction, and (3) systematic reviews establishing methodological foundations in presentation attack detection and image-based storytelling. Her recent work increasingly emphasizes explainability and ethical considerations in AI deployment. Dr. Farhood has participated in externally funded research projects, including the 2022 project "Estimating the Number of Tyres in Stockpiles" (October-December 2022). No information is available regarding students she has advised. No information is available about specific research laboratories or teams led by Dr. Farhood.

• Social Cognition
• Traumatic Brain Injury Rehabilitation
• Emotion Processing
• +۳ مورد دیگر

Professor Skye McDonald is a leading Clinical Neuropsychologist at the University of New South Wales (UNSW), School of Psychology, with over 170 peer-reviewed publications focusing on social cognition and brain injury rehabilitation. She leads the national Centre of Research Excellence in Brain Recovery 'Moving Ahead,' addressing psychosocial rehabilitation after traumatic brain injury. McDonald holds a BSc (Honors, First Class) from Monash University, an MSc in Clinical Neuropsychology (First Class Honors) from the University of Melbourne, and a PhD from Macquarie University. With 10 years of clinical experience working with individuals with acquired brain disorders prior to her academic career, she brings significant practical insight to her research. Her research program centers on understanding how the brain processes emotions and social cognition following injury, particularly how social signal interpretation breaks down after brain trauma. McDonald is an active member of the Australasian Society for the Study of Brain Impairment and serves as a core member of PsycBITE, a database cataloging evidence-based treatments for brain injury disorders. Her work spans multiple methodologies including neuroimaging, behavioral experiments, and clinical intervention development, with recent emphasis on telehealth assessment tools, cross-cultural emotion perception, and biopsychosocial frameworks for apathy. Professor McDonald's publications reveal a consistent trajectory examining social cognition assessment and rehabilitation across diverse populations including traumatic brain injury patients, dementia populations, and cross-cultural contexts. Her recent work has expanded into developing and validating brief assessment tools like the Brief Assessment of Social Skills (BASS) and implementing group-based social cognition interventions such as SIFT IT. The temporal pattern of her publications shows increasing focus on ecological validity, cross-cultural considerations, and implementation science in neuropsychological rehabilitation. As a respected leader in her field, McDonald contributes to professional discourse through her extensive publication record and leadership roles. Her work bridges clinical practice and research, with particular emphasis on translating findings into practical assessment tools and rehabilitation protocols that address real-world challenges faced by individuals with brain injuries and their families. Professor McDonald maintains an active research program with numerous current projects examining emotion recognition, social disinhibition mechanisms, and culturally adapted assessment tools. Her work on the Vietnamese Montreal Cognitive Assessment demonstrates her commitment to global applicability of neuropsychological instruments, while her leadership in the Australian Traumatic Brain Injury Initiative reflects national recognition of her expertise.

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +۵ مورد دیگر

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.

Wen Huمشاهده پروفایل

استاد

• Cyber-physical systems
• Internet of Things
• Sensor network systems
• +۳ مورد دیگر

Professor Wen Hu is a distinguished academic at the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he holds a professorship focusing on cutting-edge research in cyber-physical systems and the Internet of Things (IoT). With extensive experience in both academia and industry, Professor Hu has established himself as a leading researcher in sensor network systems, low-power communications, security, and compressive sensing. His work bridges theoretical research with practical applications, as evidenced by his active commercialization efforts through roles as Chief Technology Officer at Parking Spotz and former Chief Scientist at WBS Tech. Professor Hu's research interests span multiple domains within cyber-physical systems and IoT. His work particularly focuses on low-power wireless communications , sensor network security , compressive sensing techniques , and novel applications of IoT technologies . His research group explores how these technologies can be applied to solve real-world problems in areas such as smart buildings, environmental monitoring, and human-computer interaction. The research integrates hardware design, communication protocols, security mechanisms, and data analytics to create efficient and robust IoT systems. Professor Hu's publication record demonstrates a consistent focus on advancing the state-of-the-art in wireless sensor networks and IoT. His recent work shows increasing integration of machine learning techniques with traditional sensor systems, exploring applications in areas ranging from healthcare monitoring to agricultural technology. There's a clear trend toward more sophisticated signal processing approaches, particularly using mmWave technology and visible light communications, while maintaining the core focus on energy efficiency and security that has characterized his earlier work. CSIRO Office of Chief Executive (OCE) Julius Career Award (2012-2015) Professor Hu has successfully secured multiple research grants from the Australian Research Council, CSIRO, and industry partners, enabling his team to pursue ambitious research projects with both theoretical significance and practical applications. His editorial leadership as Editor-in-Chief of ACM TOSN and organizational roles in major conferences like CPS-IoT Week 2020 and ACM/IEEE IPSN 2023 demonstrate his significant contributions to the research community. As a senior member of both ACM and IEEE, he actively mentors students and early-career researchers while fostering collaborations across academia and industry. Professor Hu leads a vibrant research group at UNSW that combines theoretical research with practical implementation. His team works closely with industry partners to ensure research relevance and facilitate technology transfer. The group maintains strong connections with international research communities and participates in collaborative projects that address global challenges in IoT and cyber-physical systems.

Kyla Hudsonمشاهده پروفایل

پژوهشگر ارشد

• Aphasia Rehabilitation
• Communication Disability
• ICF (International Classification of Functioning, Disability and Health)
• +۳ مورد دیگر

Dr Kyla Hudson is a Research Fellow at the School of Health and Rehabilitation Sciences, The University of Queensland. She works within the Centre for Clinical Research Excellence (CCRE) in Aphasia Rehabilitation and the Communication Disability Centre, focusing on aphasia rehabilitation and the application of the International Classification of Functioning, Disability and Health (ICF) framework, particularly participation and environmental factors. PhD in Speech Pathology, The University of Queensland Master of Speech Pathology, The University of Queensland Active in research supervision and grant-funded studies Her research spans qualitative analysis of aphasia recovery, telepractice communication training, and interventions for healthcare professionals. She has led studies on preventing post-stroke depression, peer-led aphasia support, and cultural adaptation of rehabilitation strategies in Vietnam. Grants include NHMRC and National Stroke Foundation funding. Recent publications highlight trends in telepractice , communication partner training , and ICF-based rehabilitation . She emphasizes participatory approaches and environmental factors in aphasia management. Dr Hudson is available for supervision and collaborates with multidisciplinary teams across healthcare , education , and clinical research domains.

• Data Mining
• Data Science
• Data Analytics
• +۴ مورد دیگر

Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.

• Computer Vision
• Multimedia Computation
• Machine Learning
• +۲ مورد دیگر

Litao Yu is a Part-Time Lecturer and Visiting Scholar at the Faculty of Engineering and Information Technology, University of Technology Sydney. Concurrently, he serves as a Senior Data Analyst at Australia's Department of Agriculture, Fishery and Forestry. His academic appointments include Research Fellow at UTS (2019-2024) and post-doctoral positions at Griffith University and Queensland University of Technology. Education: PhD from The University of Queensland (2013-2016) MPhil from Dalian University of Technology (2009-2012) BSc from Dalian Maritime University (2003-2007) Dr. Yu's research focuses on computer vision and machine learning with applications in agriculture, multimedia systems, and multimodal learning. His work spans few-shot learning, semantic segmentation, fine-grained recognition, and multimodal fusion techniques. Key application domains include animal welfare monitoring, aquaculture quality control, and travel information enhancement. His publications demonstrate strong thematic convergence around efficient visual recognition systems , with recurring focus on few-shot learning paradigms and attention mechanisms. Recent work shows increasing emphasis on agricultural applications of computer vision, including poultry monitoring, fish processing automation, and sheep tracking. Blockchain integration for supply chain transparency represents another emerging theme. No scientific awards are mentioned in available records. Teaching responsibilities at UTS include courses on Real-time Operating Systems and Shell Programming . No information is available regarding research grants, supervised students, or laboratory affiliations.

• Machine Learning
• Artificial Intelligence
• Federated Learning
• +۳ مورد دیگر

Dr. Jing Jiang is an Associate Professor in the School of Computer Science and a core member of the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). As an ARC DECRA Fellow, she has secured over AU$2 million in research funding through multiple ARC grants, CSIRO/Data61 projects, and industry collaborations. Her work bridges theoretical advances in machine learning with practical applications across various domains. Dr. Jiang's research focuses on machine learning, particularly federated learning, reinforcement learning, and foundation models. She explores how to make these technologies work effectively in heterogeneous environments, addressing challenges like data privacy, non-IID data distributions, and efficient communication. Her work spans both theoretical foundations and practical implementations for real-world applications. Her publications demonstrate a strong trend toward personalized federated learning approaches, with significant contributions to recommender systems, time series analysis, and weather forecasting. She has developed novel techniques like variational autoencoder approaches for federated collaborative filtering and adaptive prompt learning for foundation models on devices. Dr. Jiang has received several notable recognitions: ARC DECRA Fellow Awardee of the Australian International Postgraduate Research Scholarship (IPRS) Dr. Jiang has successfully led multiple major research projects, including two ARC Discovery Projects, one ARC Linkage Project, and a CSIRO/Data61 CRP project where she served as lead Chief Investigator. She has supervised numerous PhD and Master's students and actively collaborates with industry partners on applied research. As a core member of the Australian Artificial Intelligence Institute (AAII) at UTS, Dr. Jiang contributes to a vibrant research ecosystem focused on cutting-edge AI research. She collaborates closely with Professor Guodong Long and other researchers on various machine learning projects, and serves in leadership roles including program co-chair for major conferences like ADMA2023.

• Machine Learning
• Computer Vision
• 3D Geometry
• +۲ مورد دیگر

Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.

Feng Liuمشاهده پروفایل

استادیار

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +۳ مورد دیگر

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.