جست‌وجوی استاد و پژوهشگر

Zhenghao Chenمشاهده پروفایل

مدرس

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Dr. Zhenghao Chen is a Lecturer in Data Science at the University of Newcastle, affiliated with the School of Information and Physical Sciences. He earned his Ph.D. from the University of Sydney in 2022, following a B.Eng. H1 degree from the same institution in 2017. Prior to his current position, Dr. Chen served as a Postdoctoral Research Fellow at the University of Sydney (2022-2024), a Research Engineer at TikTok (2024), and as a Visiting Research Scientist at Microsoft Research and Disney Research (2022-2023). Dr. Chen's educational background includes: Doctor of Philosophy, University of Sydney (2022) Bachelor of Information Technology (B.Eng. H1), University of Sydney (2017) Dr. Chen's research spans multiple domains within artificial intelligence, with particular expertise in Computer Vision, Natural Language Processing, and Machine Learning. His work in Generative AI has garnered significant recognition, with applications in both academic and industrial settings. His research interests are reflected in his Fields of Research percentages: Deep Learning (30%), Computer Vision (30%), Natural Language Processing (20%), and Multimodal Analysis and Synthesis (20%). His publications in top-tier venues like CVPR, ICCV, ECCV, and journals like IEEE TPAMI demonstrate the breadth and impact of his work. Analysis of Dr. Chen's recent publications (2022-2025) reveals a consistent focus on neural compression techniques, 3D perception, and multimodal AI systems. His work spans medical imaging (CXR bone suppression), video compression, point cloud processing, and neural surface reconstruction. A notable trend is his exploration of efficient AI systems that work well under resource constraints, as evidenced by his involvement in the EMCLR workshop. His research often bridges theoretical advances with practical applications across multiple domains. Dr. Chen has received several prestigious awards: Microsoft Research Asia StarTrack Fellowship (2025) ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing (2024) Australia Government Research Training Program (RTP) Fellowship (2019) Google Australia Prize for Excellence in Computer Science (2017) Dr. Chen is actively involved in the academic community, serving on the Program Committee for major AI conferences including CVPR, ICCV, ECCV, SIGGRAPH, AAAI, and others. He also organizes workshops in Multimedia and ICCV conferences, and serves as a reviewer for prestigious journals. His teaching responsibilities include courses on Intelligent Visual Signal Understanding, Video Intelligence and Compression, Database and Information Management, and Computing Fundamentals at both the University of Sydney and University of Newcastle.

Xiaohan Yuمشاهده پروفایل

مدرس

• Ultra-Fine-Grained Visual Categorization
• Smart Farming
• Automated Crop Cultivar Identification
• +۳ مورد دیگر

Dr. Xiaohan Yu is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing, joining in December 2023. Previously, he completed his doctoral studies at Griffith University and served as a Research Fellow at the ARC Research Hub for Driving Farming Productivity. His research focuses on Ultra-Fine-Grained Visual Categorization (Ultra-FGVC), Smart Farming, and Automated Crop Cultivar Identification, with over 70 publications in top-tier venues like ICCV, CVPR, and IEEE Transactions. He holds editorial roles at Pattern Recognition and SN Computer Science , and received the APRS Early Career Award (2022) and ACM MM 2024 Outstanding Area Chair distinction. Education: Completed doctoral studies in Artificial Intelligence at Griffith University, Australia. Research Interests: Ultra-Fine-Grained Visual Categorization (Ultra-FGVC) Smart Farming and Agricultural Robotics Computer Vision Applications in Healthcare (e.g., trachoma detection) Deep Learning, Continual Learning, and Domain Adaptation Key Contributions: Pioneered Ultra-FGVC research, developed frameworks like Mix-ViT and CLE-ViT, and contributed to benchmarking multi-object tracking in farming. His work bridges pattern recognition with real-world applications in agriculture and healthcare. Scientific Awards: Australian Pattern Recognition Society (APRS) Early Career Researcher Award 2022 ACM Multimedia 2024 Outstanding Area Chair Award Advising & Grants: Actively involved in editorial roles (Area Chair for ACM MM, IJCNN) and grant-funded research through ARC hubs. His work is supported by collaborations in agriculture and AI-driven solutions for crop cultivar identification. Labs & Affiliations: Member of Macquarie's Smart Green Cities Research Centre and Frontier AI Research Centre , advancing interdisciplinary AI applications.

• Artificial Intelligence
• Computer Vision
• Digital Agriculture
• +۳ مورد دیگر

Ferdous Sohel is a Professor of Information Technology at Murdoch University and inaugural lead of the Agricultural Technologies program. His research spans AI, computer vision, and digital agriculture, with applications in medical imaging and environmental monitoring. He received the Mollie Holman Doctoral Medal and Vice Chancellor's Early Career Research Award. Research Impact: Developed innovative AI models for aquaculture oxygen prediction, 3D object tracking, quantum neural networks, and prohibited item detection. His work advances precision agriculture through hyperspectral classification frameworks and irrigation decision systems. Professional Service: Associate Editor for IEEE Transactions on Multimedia and senior IEEE member. Current projects include adversarial robustness for LiDAR systems and lightweight dormitory security networks.

Leong Shu Minمشاهده پروفایل

مدرس

• Face Recognition
• Emotion Recognition
• Face Analysis
• +۳ مورد دیگر

Leong Shu Min is a Lecturer in the School of Information Technology at Monash University Malaysia. She holds a Ph.D. in IT from Monash University Malaysia (2023), focusing on privacy-preserving and emotional understanding of human faces using machine learning. She earned her Master of Engineering Science (2020) and B.Eng. (Hons) in Electronics with Computer specialization (2018) from Multimedia University. Her research emphasizes face analysis, emotion recognition, and security-related image processing. Education Ph.D., IT, Monash University Malaysia (2019–2023) M.Eng.Sc., Multimedia University (2018–2020) B.Eng., Multimedia University (2014–2018) Research Interests Her work centers on facial recognition systems, emotion analysis, and privacy-preserving techniques. She explores Local Binary Pattern algorithms and micro-expression recognition, aiming to enhance security and ethical AI applications. Recent projects include detecting synthetic music and uncovering biases in video-based emotion recognition systems. Projects Chief Investigator in the Æinstein: Adversarial AI amongst Materials Discovery Domains project (2024–2026), focusing on AI-driven material discovery and ethical AI challenges. Advising She has been accepting PhD students since 2020, mentoring research in facial analysis and machine learning applications.

• Data Integration
• Data Quality
• Recommender Systems
• +۵ مورد دیگر

Assoc Prof Henry Nguyen is an Associate Professor at Griffith University's School of Information and Communication Technology, with expertise in data integration, data quality, recommender systems, and big data visualization. He directs the Responsible Big Data Lab and has secured over $3.5M in funding since 2015 from ARC, DFAT, and industry partners. PhD & Master's from EPFL, Switzerland ARC DECRA Award (2020) His research focuses on privacy-preserving AI for social data , IoT , and satellite analytics , with over 200 publications in top venues like SIGMOD, KDD, and IEEE TKDE. Recent work spans federated learning , graph neural networks , and secure AI systems . Article trends highlight 2024-2025 publications on: Federated recommendation security On-device AI optimization Privacy-preserving explainable AI Graph condensation techniques LLM-powered risk analysis Cloud-edge collaboration Scientific contributions include ARC DECRA Award 2020 Multiple senior PC roles in A* conferences Citations in International AI Safety Report 2025 Henry Nguyen supervises 12 active PhD/MSc students and has directed 8 completed doctoral theses . His funded projects include collaborations with Ubitech , KARI , and CSIRO , focusing on Australia-Korea partnerships and responsible AI development.

• Educational Technology
• Teacher Education
• ICT in Education
• +۳ مورد دیگر

Geoffrey Ian Romeo is a former academic researcher affiliated with the Australian Catholic University (ACU), focusing on educational technology and teacher education. His work emphasizes integrating technology into teacher training programs, particularly in remote and rural settings. Key contributions include studies on TPACK (Technological Pedagogical Content Knowledge), web conferencing for pre-service teacher practicum, and the use of video technology for classroom observation and supervision. Research interests revolve around ICT applications in education, teacher professional development, and technology-mediated learning environments. Notable projects include the Teaching Teachers for the Future (TTF) initiative, exploring how technology enhances teacher education. Publications span journals like the Asia-Pacific Education Researcher and Australian Educational Computing , alongside conference proceedings and book chapters addressing challenges and innovations in educational technology. Romeo's articles highlight trends in leveraging technology to bridge geographic gaps in teacher training, evaluating interactive tools like web conferencing and video analysis, and rethinking educational practices through ICT integration. Despite no listed awards or grants, his work underscores practical and theoretical advancements in preparing educators for technology-rich classrooms.

Lillian Wangمشاهده پروفایل

مدرس

• Cloud e-learning
• Educational Technology
• IoT
• +۳ مورد دیگر

Lillian Wang is a Lecturer at the School of Information Technology, Monash University Malaysia. She holds a PhD in IT from Multimedia University (2020), MEngSc from Multimedia University (2012), and BSc (Hons) in Software Engineering (2006). With 15+ years of experience, she has served as an educator, trainer in educational technologies (e.g., Google Education, Blended Learning), and reviewer for international journals/conferences. Research focuses on Cloud e-learning, IoT in wastewater treatment, and security/privacy in educational platforms. Contributions to UN SDGs: Education (4) and Clean Water & Sanitation (6). Active in IoT-based solutions for healthcare (medication dispensing), smart attendance systems, and environmental monitoring. Her work includes 19+ peer-reviewed publications and collaborations across multiple disciplines. She is currently accepting PhD students in VR in education and AI/ML in wastewater management.

• Biomedical Data Computing
• Medical Image Processing
• Image Registration
• +۴ مورد دیگر

Associate Professor Xiu Wang is affiliated with the School of Computer Science at The University of Sydney, where she serves as Associate Director of the Multimedia Lab and a member of the Biomedical & Multimedia Information Technology (BMIT) Research Group. Her research focuses on panoramic data analysis, biomedical computing, image processing, and medical data fusion. Key projects include tumor treatment outcome prediction using deep learning, collaborative learning of multimodal medical imaging, and deformable image registration techniques. She teaches courses such as COMP5214 (Software Development in Java) and supervises graduate students in AI-driven medical diagnostics and imaging. Notable collaborations involve Dr. Hui Cui and Mr. Chaojie Zheng. Her work bridges computer science and healthcare, emphasizing AI applications in oncology, radiology, and neuroimaging. Recent articles highlight advancements in MRI segmentation, PET/CT radiomics, and graph neural networks for disease diagnosis. She actively contributes to interdisciplinary research, integrating computational methods with clinical data for precision medicine.

• Deep Learning
• Medical Image Analysis
• Bio Data Analysis
• +۵ مورد دیگر

Dr. Sicily Fung Fung Ting is a Lecturer at the School of Information Technology (SoIT), Monash University. She earned her Ph.D. in Engineering (2020) from Multimedia University, specializing in breast cancer classification and visualization using deep neural networks. Her research focuses on deep learning applications in medical image analysis, expert systems, and biomedical engineering. Notably, her work has been recognized with the 2020 WSIS Prize (Champion) and the 2023 FIT Dean's Education Excellence Award. Education: Ph.D., Engineering (2020), Multimedia University MEng.Sc., Visualisation for Early Brain Infarct Detection (2017), Multimedia University BEng (Hons), Electronics & Robotics (2014), Multimedia University Research Interests: Deep Learning for Medical Imaging Computer-Aided Detection (CAD) Telehealth Systems Biomedical Signal Processing Key Projects: FRGS 2022: RewardQNet Framework for Intracranial Aneurysm Segmentation GEMS 2025: AI-Driven Neuroimaging Tool for Aneurysm Detection Her publications span journals like Expert Systems with Applications and Computerized Medical Imaging and Graphics , with impactful contributions to breast cancer classification and brain lesion segmentation. She actively engages in academic outreach, including public lectures and industry collaborations.

Lim Jit Yanمشاهده پروفایل

مدرس

• Few-Shot Learning
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Lim Jit Yan is a Lecturer at the School of Information Technology, Monash University Malaysia. He holds a PhD in Information Technology (2023) from Multimedia University, specializing in self-supervised metric-based meta-learning for few-shot image classification, and a B.IT (Hons) in Artificial Intelligence (2019) from the same institution. His research focuses on few-shot learning, computer vision, and deep learning, with contributions to medical image analysis (e.g., Covid-19 detection), transfer learning applications, and generative models. He has published extensively since 2021, with notable work on self-supervised feature fusion and prototypical networks for few-shot learning. Research interests include few-shot learning techniques, neural architecture design for image classification, and real-world applications like healthcare diagnostics and autonomous systems. Recent work emphasizes self-supervised learning and transformer-based models to address data scarcity challenges in AI. No formal scientific awards are listed. Advising details are not provided in the text, though his publications suggest collaborative research with colleagues like Lim K.M. and Lee C.P. His research spans theoretical advancements and applied projects, including work on pill image recognition, traffic sign detection, and brain tumor classification.

Lydia Cuiمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Biomedical Image Processing and Analysis
• Machine Learning
• +۶ مورد دیگر

Dr. Lydia Cui is a Senior Lecturer at La Trobe University's Department of Computer Science and Information Technology. She holds a PhD and MPhil from the University of Sydney and a Bachelor's from Harbin Institute of Technology. Her research focuses on AI-driven biomedical image analysis, machine learning, and precision oncology, with emphasis on multi-modality imaging fusion, cancer diagnosis, and graph neural networks. She actively collaborates with industry and hospitals to translate AI technologies into clinical workflows. Dr. Cui leads the Department’s Teaching & Learning and Postgraduate Course Coordination roles. She has received notable awards, including the SNMMI 2015 International Best Paper Award. Her teaching includes Data Mining, Computer Vision, and Image Processing courses. Research interests include segmentation of biomedical images, AI for disease prognosis, and integration of imaging with non-imaging biomarkers. Recent publications span top-tier journals like IEEE Transactions on Medical Imaging and conferences such as MICCAI. She supervises students in AI and biomedical informatics. Funded projects include 'Multi-modality data-driven health monitoring in Industry 4.0' with Rudder Technology.

• Quantum Communications
• Satellite Communications
• Telecommunications
• +۵ مورد دیگر

Xiaoyu Ai is a Researcher at the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW Kensington). His work bridges telecommunications engineering with quantum communication technologies. Bachelor of Engineering (Xidian University, 2013) Master of Engineering Science (UNSW, 2015) PhD in Telecommunications (UNSW, 2022) A specialist in quantum key distribution (QKD), Xiaoyu Ai focuses on channel coding for satellite-based QKD systems, including the development of LDPC codes and multithreaded reconciliation algorithms. His research extends to wireless communication protocols for mining IoT applications and scalable LoRa mesh networks. Recent work includes quantum emitter integration in hexagonal boron nitride for secure communication hardware. Key publications highlight his contributions to quantum cryptography, satellite communication, and industrial IoT. Notable projects include the CRC-P initiative for LoRa-based backup systems in the mining industry and collaborations in quantum networking with institutions like University of Technology Sydney. His technical expertise spans secure communication protocols, photonic device optimization, and low-light multimedia algorithms, as evidenced by recent conference presentations.

Cunjian Chenمشاهده پروفایل

عضو هیئت علمی

• Computer Vision
• Deep Learning
• Medical Imaging
• +۴ مورد دیگر

Cunjian Chen is an Adjunct Lecturer at the Department of Data Science & AI, Monash University, and a Research Fellow at Monash University Suzhou. He holds a PhD in Computer Science from West Virginia University and serves as an Associate Editor for journals like Neural Processing Letters and IET Image Processing. His research focuses on computer vision, deep learning, adversarial machine learning, and cross-spectral biometric systems. Notable contributions include advancements in thermal-to-visible face recognition, medical image segmentation, and robust adversarial defense mechanisms. Dr. Chen has received the Outstanding Area Chairs award at ICME 2021. He actively contributes to academic governance roles, including Tutorial Chair for IJCB and Area Chair for ICME, ICIP, ICASSP, IJCNN, and FG. He is currently accepting PhD and research assistant students in computer vision and deep learning, emphasizing innovative applications in security and healthcare. His research collaborations span global institutions, with recent work addressing challenges in cross-spectral imaging, medical AI, and adversarial robustness. Professional activities include patent engagements with the Monash Suzhou Research Institute. For further details, visit his personal website or Google Scholar profile .

Soon Lay Kiمشاهده پروفایل

دانشیار

• Applied Natural Language Processing
• Data Management
• Aspect-Based Sentiment Analysis
• +۹ مورد دیگر

Soon Lay Ki is an Associate Professor at the School of Information Technology, Monash University Malaysia, where she also serves as Associate Head (Graduate Research) since November 2018. Her academic journey began with roles at Multimedia University (MMU), where she was a Senior Lecturer and Deputy Dean (Research and Innovation) from 2016 to 2018. PhD in Web Engineering, Soongsil University, Korea Master of Science in Database, Universiti Putra Malaysia Bachelor of Computer Science, Universiti Putra Malaysia Her research centers on applied natural language processing and data management , with a focus on analyzing domain-specific and social media content. Her work spans aspect-based sentiment analysis , cyberbullying detection , misinformation detection , and relation extraction from conversational texts. Recently, her research has expanded into digital health , particularly emotion-aware mental health chatbots and emotion detection via video data. The most recent articles highlight a strong trend in AI for social good , including legal reasoning, mental health, accessibility, and public health. Her publications appear in high-impact journals and conferences such as Artificial Intelligence and Law , IEEE Transactions on Dependable and Secure Computing , and ACL-affiliated workshops. She has received notable scientific awards, including: ITEX'24 Silver Award for 'MOBOT' mental health chatbot (2024) Silver Medal at Malaysia Technology Expo 2023 for the same innovation The Incubator Grant: Bolster Category (2023) Dr. Soon has graduated seven PhD and three Master’s students, one of whom received the MMU Best Master Thesis Award in 2015. She leads multiple research grants, including FRGS-funded projects and industry collaborations with Telekom Malaysia and Intel . She is currently a Chief Investigator or Primary Chief Investigator on six active projects, including WHinc, WAge, and Epsilon, often in collaboration with Monash Australia and SEACO. She is part of key research teams such as the Action Lab at Monash University Australia and the South East Asia Community Observatory (SEACO) , contributing to inclusive research infrastructure and public health data access initiatives.

Ramanathan Subramanianمشاهده پروفایل

دانشیار

• Human-centered computing
• AI systems
• Non-verbal behavioral analysis
• +۷ مورد دیگر

Ramanathan Subramanian is an Associate Professor in the Department of Information Technology & Systems, AI and Robotics at the University of Canberra. His past affiliations include IIT Ropar and UIUC, Singapore. He has received notable awards including the IEEE Transactions on Affective Computing Best Paper Award (2019) and Multimedia Rising Star nomination (2015). His research focuses on Human-centered computing, AI systems leveraging non-verbal behavioral cues, and applications in health analytics and virtual reality. He actively supervises PhD and Master’s students in areas like Human-Computer Interaction, Data Science, and VR for healthy aging. Research interests: Human perception modeling, intelligent interfaces, health-focused VR, and applied machine learning Key projects: Driver sentiment prediction, distraction awareness campaigns, and robotics for waste management His work contributes to UN SDGs related to health and well-being, particularly through technologies improving aging care and mental health monitoring. Recent projects include developing explainable AI for emotion inference and multimodal data fusion systems. Awards: IEEE Best Paper Award, Multimedia Rising Star Grants: Lead or Co-Investigator on three major grants totaling over AUD 4M Labs/Teams: Collaborates extensively with the University's robotics and health tech groups, contributing to interdisciplinary projects in AI ethics and medical signal processing.