جستجوی استاد راهنمای دانشگاهی

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.

• Face Recognition
• Emotion Recognition
• Face Analysis
• +۳ 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.

• Biomedical Data Computing
• Medical Image Processing
• Image Registration
• +۴ 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.

• Deep Learning
• Medical Image Analysis
• Bio Data Analysis
• +۵ 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.

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

Dr. Anthony Bartel is an Adjunct Industry Fellow at the School of Social Sciences, Media, Film and Education, Swinburne University of Technology. He teaches Physics, Electronic Instrumentation, and Multimedia Technology to undergraduate and postgraduate students across science, engineering, games, and multimedia disciplines. He has coordinated multiple subjects and courses in these areas. His educational background includes an undergraduate major in Physics, a Master's involving instrumentation for balloon-borne x-ray astronomy, and a PhD in neutron physics instrumentation development. Undergraduate: Physics Master's: Instrumentation for Balloon-Borne Astronomical X-Ray Observatory PhD: Instrument Development for Neutron Physics Experiments Dr. Bartel's research spans Artificial Intelligence, Curriculum and Pedagogy, Control Engineering, Mechatronics, Robotics, and Music Education Technology. His work emphasizes the use of AI and serious games to enhance deliberate music practice and self-regulated learning. He explores feedback models, incentive systems, and progression design in mobile music games to improve educational outcomes. His recent publications (2010–2021) demonstrate a strong trend toward integrating AI with music education, particularly in generating adaptive sight-reading exercises and analyzing percussive sounds. The research bridges computer science, education, and acoustics, focusing on intelligent tools for music practice and systematic sound description. He is available to supervise PhD students and has previously supervised doctoral research in intelligent music tools and percussive audio lexicons. Intelligent Tools for Deliberate Music Practice: Evolving Targeted Sight Reading Exercises (2015–2019) The Percussive Audio Lexicon (2014–2015) Dr. Bartel has not received any publicly listed scientific awards. His teaching and research are supported through institutional roles and collaborative projects in educational technology and instrumentation. He is involved in interdisciplinary teams working on serious games for music education and AI-driven learning systems, contributing expertise in instrumentation, signal processing, and pedagogical design.

• Artificial Intelligence
• Curriculum and Pedagogy
• Control Engineering
• +۶ 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.

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

Associate Professor Wong Kok Sheik is the Deputy Head (Research) at the School of Information Technology, Monash University Malaysia. He holds a Doctor of Engineering from Shinshu University (Japan) and advanced degrees in Computer Science and Mathematics from Utah State University (USA). His research focuses on multimedia signal processing, cybersecurity, and digital health, with contributions to data hiding, encryption, and smart grid security. He leads a EU-funded WAge project on post-pandemic workplace health interventions. Education: PhD in Engineering, Shinshu University, Japan (2009) Masters in Computer Science & Mathematics, Utah State University, USA (2005-2004) Bachelor of Science, Utah State University, USA (2002) Professional Roles: Associate Editor, IEEE Signal Processing Letters Member, IEEE Signal Processing Society’s IFS Technical Committee Board Member, APSIPA Multimedia Security and Forensics (MSF) Committee His research interests span multimedia forensics, encrypted domain processing, and cybersecurity frameworks. Notable projects include recovery of missing coefficients in compression standards and HDR imaging enhancement. Recent work integrates digital health, addressing workplace mental/physical health in post-pandemic environments. Publications reflect expertise in watermarking, encryption, and biometric security. Key contributions include encryption-resistant data embedding and secure smart grid analytics. Awards include IEEE CES Service Awards (2015, 2016) and Best Paper recognitions. He supervises over 20 PhD/MSc students, focusing on topics like biometric recognition, data hiding, and anomaly detection. Grants include a €2M EU Horizon 2020 project (IDENTITY) and a RM146k smart grid initiative. His teaching emphasizes foundational IT research methods and theoretical computer science.

• Multimedia Signal Processing
• Cybersecurity
• Digital Health
• +۳ 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.

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

Dr. Yangyang Shu serves as an Associate Lecturer in the School of Systems and Computing at the University of New South Wales (UNSW) Canberra campus. Previously, he held a Research Fellow position at the Australian Institute for Machine Learning (AIML) at the University of Adelaide, where he contributed to the Centre for Augmented Reasoning (CAR) project. His academic foundation includes a Ph.D. in Computer Science from the University of Technology Sydney. His research spans cutting-edge areas in artificial intelligence, with core expertise in Machine Learning and Computer Vision . Key focus areas include: Low-supervised paradigms (Weakly/Semi-/Self-Supervised Learning) Rationale-Guided Machine Learning systems Generative AI applications Machine Learning in Music and affective computing Fine-grained visual recognition in data-scarce environments Dr. Shu's publication record demonstrates consistent contributions to top-tier venues including CVPR, ECCV, IEEE Transactions on Multimedia, and Pattern Recognition. His work bridges theoretical advances in learning with practical applications in photo aesthetic assessment, semantic segmentation, and emotion recognition, showing particular strength in developing methods for low-data regimes and leveraging privileged information. He maintains active research collaborations through the School of Systems and Computing at UNSW Canberra, building on prior affiliations with AIML and the Centre for Augmented Reasoning. His current teaching responsibilities include ZEIT 2103 (Data Structures and Representation) for Semester 1, 2025.

• Machine Learning
• Computer Vision
• Weakly Supervised Learning
• +۷ مورد دیگر

Professor Zhiyong Wang is a faculty member at the School of Computer Science, University of Sydney, serving as Deputy Director of Sydney Informatics Hub and Director of the Multimedia Computing Laboratory. His research focuses on multimedia computing, including information retrieval, computer vision, AI, and applications in agriculture, healthcare, and environmental monitoring. He holds a B.Eng., M.Eng. from South China University of Technology, and a Ph.D. from The Hong Kong Polytechnic University. His work bridges theoretical advancements and real-world applications, with over 200 peer-reviewed publications. He is a member of IEEE and former President of Australia Pattern Recognition Society (APRS). Research Interests: Enabling computers to understand and create multimedia content, with emphasis on human-centric computing, remote sensing, and interdisciplinary applications. Notable areas include action recognition, medical image analysis, and environmental monitoring. Current Roles: Leads the Multimedia Computing Lab, collaborates with Sydney Institute of Agriculture and Brain and Mind Centre. Teaches COMP5216 (Mobile Computing), COMP5405 (Digital Media Computing), and COMP5425 (Multimedia Retrieval). Students: Supervises 9 Ph.D. students focusing on topics like precision agriculture, biomedical engineering, and AI-driven video analytics. Key projects include soil moisture forecasting, biomechanical risk analysis for knee osteoarthritis, and explainable machine learning.

• Multimedia Computing
• Computer Vision
• Computer Graphics
• +۷ مورد دیگر

Dr. Jing Zhang is a Lecturer in the School of Computing at the Australian National University (ANU) , within the ANU College of Systems & Society . Previously, he served as a Research Fellow at ANU (2021–2022) and earned his PhD in 2021 under the supervision of Nick Barnes. His academic journey includes a Master’s (2010) and Bachelor’s (2007) from Northwestern Polytechnical University . Education: PhD, Australian National University, 2021 (Supervisor: Nick Barnes) Master’s Degree, Northwestern Polytechnical University, 2010 Bachelor’s Degree, Northwestern Polytechnical University, 2007 Research Interests: Jing Zhang’s research focuses on computer vision and machine learning , with a particular emphasis on generative AI . His work addresses challenges in image, video, and audio generation/editing; explainable model adaptation; out-of-distribution detection; and adversarial attacks/defenses. He explores techniques to enhance model robustness and generalization across diverse domains. Awards & Recognition: CVPR 2020 Paper Award Nominee for “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders” Teaching & Supervision: Jing Zhang teaches COMP8536: Advanced Topics in Computer Vision (Semester 2, 2024) and ENGN4528/6528: Computer Vision (Semester 1, 2025). He currently supervises 4 PhD students as their primary advisor, focusing on cutting-edge research in computer vision and AI. Professional Contributions: Co-organizer of tutorials on salient object detection (ACCV 2019) and uncertainty estimation (ICCV 2021) Regular reviewer for top venues: CVPR, ICCV, ECCV, TPAMI, IJCV, and others Research Groups: While not explicitly stated, his work is likely affiliated with ANU’s computer vision research groups, focusing on generative AI and robust machine learning systems.

• Computer Vision
• Machine Learning
• Generative AI
• +۵ مورد دیگر

Prof. Tao Gu is a Professor at the Department of Computing, Macquarie University, Sydney. He holds affiliations with the Future Communications Research Centre, Hearing Research Centre, and Smart Green Cities Research Centre. His research focuses on IoT, Ubiquitous Computing, Mobile Computing, Embedded AI, Wireless Sensor Networks, and Big Data Analytics. He earned his Ph.D. in Computer Science from the National University of Singapore (2006), M.Sc. in Electrical and Electronic Engineering from Nanyang Technological University, and B.Eng. in Automatic Control from Huazhong University of Science and Technology. Education: PhD (NUS), MSc (NTU), B.Eng (HUST) Affiliations: School of Computing, Macquarie University Research Themes: IoT, Embedded AI, Wireless Communication His research emphasizes innovative sensing and connectivity solutions. Notable projects include LoRa network optimization, mobile deep learning frameworks (MDLdroid), and acoustic-based health monitoring systems. He has received awards such as the IEEE SMARTCOMP 2016 Best Paper Award and the Ten Years CoMoRea Impact Paper Award at PerCom 2013. Current work explores secure device pairing, energy-efficient LoRa protocols, and multi-modal sensing using WiFi/RF signals. His lab develops systems like AudioGuard for intrusion detection and AIMSafe for driver behavior analysis. Ongoing projects address data-driven economies, hearing healthcare analytics, and non-intrusive human activity sensing.

• Internet of Things
• Ubiquitous Computing
• Mobile Computing
• +۳ مورد دیگر

Pei-Lee Teh is a Professor and Head of Department of Management at the School of Business, Monash University . She also directs the Gerontechnology Laboratory , the first of its kind in Malaysia, and founded the IEEE TEMS Malaysia Chapter . Her research bridges gerontology, technology, and management. Bachelor of Information Technology (Multimedia University, 2003) Master of Management (University Putra Malaysia, 2006) PhD in Management (Multimedia University, 2010) Her interdisciplinary research appears in journals like Information & Management , Gerontology , and Expert Systems with Applications , focusing on Management Information Systems , Gerontechnology , and Innovation in Aging . Recent work includes soft robotic grippers , age-friendly smart canes , and mobility solutions for cognitive frailty . She has secured over RM10 million in grants from entities like WHO and Malaysian ministries. Scientific awards include multiple Excellence Awards (2018-2023), Best Paper Awards (HCI, BAM), and the 2019 AACSB ICAM Innovations That Inspire recognition. She serves as Associate Editor for journals like Decision Sciences Journal of Innovative Education and Gerontechnology Journal . As an educational innovator , she integrates field trips with companies like Hewlett Packard into her teaching of courses such as International Management and Management Information Systems . Her work aligns with UN SDGs for Quality Education and Industry Innovation .

• Management Information Systems
• Technology Management
• Total Quality Management
• +۳ مورد دیگر

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.

• Image Processing
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Dr. Tan Chee Keong is a Senior Lecturer at the School of Information Technology, Monash University, Malaysia. He holds a PhD in Information and Communication Technology from Multimedia University (2014). His expertise lies in network system design, with a focus on software-defined networks, beyond 5G networks, indoor positioning systems, game theory, machine learning, and data science. He has secured over RM 800K in research grants as a principal or co-investigator and contributed to over 10 international journal papers. Notably, he received the Best Teaching Award from Multimedia University in 2016. Research activities include projects funded by government and industry, such as AI-Driven Energy Management for Smart Cities and IoT-based energy monitoring systems. His work addresses challenges in network efficiency, indoor positioning, and smart city technologies. Dr. Tan has collaborated on projects involving UAV swarming intelligence, reinforcement learning, and autonomous systems. Education: B.Eng (Hons) in Electronics (Telecommunication), Multimedia University, 2006 M.Eng.Sc (Information and Communication Technology), Multimedia University, 2009 Ph.D (Information and Communication Technology), Multimedia University, 2014 Research Interests: Optimizing wireless networks and protocols Indoor positioning and trajectory tracking Game theory applications in network resource management Machine learning for signal processing and localization Recent publications (2023–2025) emphasize AI-driven solutions for energy efficiency, path planning in disaster environments, and blockchain-based logistics optimization. These reflect a trend towards integrating AI/ML with traditional networking challenges. Awards: Best Teaching Award, Multimedia University (2016) Grants & Projects: AI-Enabled Energy Management (RM 800K+) IoT-Based Energy Monitoring (2022–2025) Smart City IoT Applications He plays a key role in industry-academia collaborations, particularly in Malaysia’s tech sector. His labs focus on developing practical solutions for smart city infrastructure and next-generation communication systems.

• Software-defined networks
• Beyond 5G networks
• Indoor positioning systems
• +۸ مورد دیگر