I took two of his courses and really liked him as a professor, especially when he incorporated real-world industry examples into his teaching. He was also very supportive, always replying to my emails and addressing my questions effectively.
very supportive and attentive in checking in with individuals and groups, remembers details and seems to genuinely care about his students and their success, my only small criticizm is he essentially just reads every lecture slide word for word and doesnt often go in depth and explain concepts or diagrams, however the course I was in was quite an easy one that seems to not require that much indepth knowledge so I cant speak for more advanced topic. overall, would recommend.
Always supportive and understanding.
Always supportive and deeply knowledgeable.
Challenges students to grow and excel.
Creates a positive and motivating atmosphere.
Dr. Zhenghao Chen is a Lecturer (Assistant Professor) in Data Science at the School of Computer and Information Sciences, University of Newcastle. He obtained his Bachelor of Information Technology with First Class Honours (B.IT. H1) in 2017 and Doctor of Philosophy (Ph.D.) in 2022 from the University of Sydney. Following his PhD, he held positions as a Postdoctoral Research Fellow at the University of Sydney from September 2022 to May 2024, Research Engineer at TikTok Australia from May to October 2024, Visiting Research Scientist at Disney Research Switzerland from October 2022 to February 2023, and Visiting Research Scientist at Microsoft Research China from October 2025 to December 2026. Dr. Chen's academic excellence has been honored with the Microsoft Research Asia StarTrack Fellowship in 2025, ACM SIGMM Award for Outstanding PhD Thesis in Multimedia Computing, Communications and Applications in 2024, Australia Government Research Training Program (RTP) International Fellowship in 2019, and Google Australia Prize for Excellence in Computer Science in 2017.
Dr. Chen's research interests lie broadly in Generative AI (GenAI), with specific emphases on computer vision (30%), deep learning (30%), multimodal analysis and synthesis (20%), and natural language processing (20%). He has published extensively at flagship conferences including CVPR, ICCV, ICLR, ECCV, MM, and AAAI, and in top journals such as T-IP, T-PAMI, T-MI, and PR. Key publications include 'Neural Video Compression with Spatio-Temporal Cross-Covariance Transformers' (2023), 'LSVC: A Learning-based Stereo Video Compression Framework' (2022), '3D Gaussian Splatting Data Compression with Mixture of Priors' (2025), 'Medxchat: A Unified Multimodal Large Language Model Framework Towards CXRS Understanding and Generation' (2025), and 'Frame-Voyager: Learning to Query Frames for Video Large Language Models' (2025). He serves on program committees for CVPR, ECCV, ICCV, SIGGRAPH, AAAI, IJCAI, MICCAI, MM, and KDD, reviews for T-IP, T-MI, T-CSVT, IJCV, PR, and acts as Guest Editor for MTAP, MDPI-Algorithms, and Frontiers-in-AI. Through roles at Microsoft, TikTok, and Disney Research, his innovations have resulted in multiple patents and deployments in global enterprise GenAI systems, while interdisciplinary collaborations feature in Nature Portfolio journals.
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