Weidong Cai

Associate Professor Weidong Cai serves in the School of Computer Science within the Faculty of Engineering at the University of Sydney, where he holds the positions of Director of the Multimedia Laboratory and Associate Director of the Biomedical & Multimedia Information Technology (BMIT) Research Group. He obtained his PhD in Computer Science from the Basser Department of Computer Science at the University of Sydney in 2001. Since completing his doctorate, Cai has remained at the University of Sydney, progressing to his current associate professorship. In 2014, he acted as Lead Investigator and Visiting Professor on medical image analysis and medical computer vision at Harvard Medical School. His academic career emphasizes interdisciplinary applications of computational methods in biomedical and multimedia domains.

Cai's research interests encompass computer vision, medical image computing, machine learning, image and video processing, multimedia computing, pattern recognition, computer graphics, bioimage informatics, and computational neuroscience. He has co-authored over 400 publications, many appearing in premier conferences such as AAAI, CVPR, MICCAI, ACM MM, WACV, and IPMI. Key recent publications include "Gotta Hear Them All: Towards Sound Source Aware Audio Generation" (AAAI 2026), "HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology" (AAAI 2026), "UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning" (AAAI 2026), "ChoreoMuse: Robust Music-to-Dance Video Generation with Style Transfer and Beat-Adherent Motion" (ACM MM 2025), and "Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness" (IPMI 2025). His scholarship has accumulated more than 12,000 citations, as reported on ResearchGate, and an h-index of 48 per Scopus, underscoring his influence in advancing machine learning techniques for medical imaging and multimodal data analysis. Cai has earned recognition through awards including the IEEE ISBI Best Paper Award Finalist for "SupWMA" (2022), Google Publication Prize for "A Multi-Stage Discriminative Model for Tumor and Lymph Node Detection" (2012), RSNA 2021 Brain Tumor AI Challenge Top Solution Prize for "HNF-Netv2", and multiple IEEE and MICCAI travel and best paper awards. He actively contributes to the field via program committees for AAAI and ECAI, and supervises award-winning PhD students.

Professional Email: tom.cai@sydney.edu.au