جستجوی استاد راهنمای دانشگاهی

Asad Malik is a Lecturer at the School of Information Technology, Monash University, Malaysia, since December 2023. Previously, he served as an Assistant Professor in the Department of Computer Science at Aligarh Muslim University, India, for two years. He holds a Ph.D. in Information Security from Southwest Jiaotong University, China (2020), an MCA from Jamia Millia Islamia (2015), and a BSc (Hons.) in Computer Application from Aligarh Muslim University (2012). His research focuses on Multimedia Forensics and Security, Deep Learning for Cyber Security, and DeepFake detection. He is a technical member of the Asia-Pacific Signal and Information Processing Association (APSIPA) and actively reviews for top journals and conferences. His work aligns with UN Sustainable Development Goals, particularly in advancing Education and Innovation. Malik’s publications (34+ outputs) emphasize robust watermarking techniques, reversible data hiding in encrypted media, and AI-driven solutions for security and environmental challenges. Recent trends highlight innovations in deepfake detection, transformer-based audio classification, and AI applications in renewable energy modeling. He is currently accepting PhD students and collaborates on projects combining cybersecurity, digital forensics, and machine learning. No notable awards are listed, but his research contributions reflect significant academic engagement in information security domains.

• Multimedia Forensics and Security
• Image Processing
• Deep Learning
• +۴ مورد دیگر

Shaun Cloherty is a Lecturer in the Department of Biomedical Engineering at RMIT University's School of Engineering. He holds a BE (Hons) in Aerospace Avionics from Queensland University of Technology and a PhD in Biomedical Engineering from the University of New South Wales. His research focuses on systems neuroscience, integrating experimental and computational methods to understand neural coding, information processing in the brain, and the neural basis of perception and behavior. Cloherty has held research positions at UNSW, Australian National University, National Vision Research Institute, and Monash University. His teaching interests include undergraduate courses such as Image Processing, Computer Architecture, and Biomedical Signal Analysis, alongside postgraduate courses in Image Systems Engineering. He is open to supervising Masters and PhD students in areas like neural prosthetics, retinal implantation safety, and motor imagery recovery. Cloherty's research spans topics including retinal prosthesis safety, visual processing in primates, and the application of virtual reality in post-stroke rehabilitation. His work has been published in journals like *Progress in Biomedical Engineering* and *Journal of Neuroscience*. He actively contributes to collaborative projects and maintains a GitHub profile for software development related to mission control training and biomedical systems. His research projects include investigating motor imagery and VR for stroke recovery, developing chronic neural electrode arrays, and optimizing solar cell light-trapping designs. Cloherty's interdisciplinary approach bridges engineering and neuroscience, with a focus on translational applications in health technology.

• Systems Neuroscience
• Neural Coding and Information Processing
• Biomedical Engineering
• +۴ مورد دیگر

Leibo Liu is a Research Fellow at the Centre for Big Data Research in Health (CBDRH) at the University of New South Wales (UNSW) Sydney and a Data Scientist at The George Institute for Global Health (TGI). Education: PhD in Big Data Research in Health from Centre for Big Data Research in Health (CBDRH), University of New South Wales (UNSW), Australia Research Interests: Liu specializes in applying natural language processing (NLP) and deep learning to extract information from unstructured health data, particularly clinical notes. His key research areas include de-identification and clinical coding of free-text data from electronic medical records (EMRs), and predictive modeling using brain imaging data with convolutional neural networks and transformer-based architectures. He developed an end-to-end deep learning framework for removing personally identifiable information (PII) from health data, implemented in the Cardiac Analytics and Innovation (CardiacAI) project. Current Projects: Next-generation Clinical Registry project at CBDRH Outcome PredicTion in IntraCerebral Hemorrhage Study (OPTICS) at TGI (leads machine learning model development) Professional Background: With expertise spanning machine learning, software engineering, and big data analysis, Liu brings a unique combination of skills to health data science. His publication record includes 1 Book Chapter, 9 Journal articles, 1 Conference Paper, 1 Conference Abstract, and 6 Preprints.

• health data science
• artificial intelligence
• machine learning
• +۳ مورد دیگر

Dr. Eric Howard is a Research Fellow at Macquarie University , affiliated with the School of Engineering , School of Mathematical and Physical Sciences , and School of Computing . His research spans interdisciplinary domains at the intersection of quantum physics, machine learning, and AI-driven systems. Key research themes include: Quantum cryptography for Industry 4.0 security Machine learning in IoT temperature sensing Adversarial AI in cybersecurity 6G wireless communication optimization Quantum information processing Deep learning for data imputation Recent publications demonstrate a focus on emerging technologies, with articles on quantum Bayesian inference , 6G signal processing , and smart city IoT systems . His collaborative work extends to blockchain-enabled supply chain visibility and generative AI applications in programming. Research collaborations span institutions in India (AIP Publishing) and Australia, with technical contributions to quantum dynamics, neural network applications, and nanosensor development.

• Quantum Physics
• Machine Learning
• Gravitational Waves
• +۴ مورد دیگر

Guodong Long is an Associate Professor at the University of Technology Sydney (UTS) in the Faculty of Engineering and Information Technology. He joined UTS in 2010 and earned his PhD there in 2014. His research focuses on federated learning, trustworthy AI, and pre-trained foundation models with applications in healthcare, IoT, and social media. PhD in Artificial Intelligence, University of Technology Sydney (2014) Leading the Foundation Model and Federated Learning research group (https://www.fmfl.group/) His work addresses challenges in frequency transformation for time series, privacy-preserving healthcare analytics, and spatio-temporal traffic forecasting. He has published extensively at top AI conferences like AAAI, ICLR, and NeurIPS, with significant citation impact (4,682 citations in 2022). Collaborations with industry partners have secured over $4M in external funding. Recent publications emphasize federated foundation models (ICLR'25), privacy-preserving recommendation systems (WWW'25), and adaptive time series analysis. His research integrates domain knowledge and graph learning for multivariate time series imputation and traffic prediction. Dr. Long actively contributes to academic leadership as General Co-Chair for WebConf 2025, Program Co-Chair for AI conferences, and reviewer for top venues. He supervises PhD and master's students and welcomes research visitors for extended collaborations.

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +۸ مورد دیگر

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
• +۳ مورد دیگر

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
• +۶ مورد دیگر

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
• +۴ مورد دیگر