Dr. Hao Zheng | Video understanding | Best Researcher Award

The University of Auckland, New Zealand 🏆

Hao Zheng is a dedicated PhD candidate at the Department of Mechanical and Mechatronics Engineering, The University of Auckland. His research expertise lies in video understanding, multi-modal large language models, generative AI, and human-robot collaboration, with a focus on human-centric manufacturing. Throughout his academic journey, he has contributed significantly to the advancement of intelligent automation, earning recognition through prestigious scholarships and multiple high-impact publications. Hao is also actively involved in research projects that integrate AI-driven technologies into manufacturing and human-robot interaction, aiming to enhance industrial efficiency and safety.

Profile

Google Scholar

Education 🎓

Hao Zheng pursued his PhD at The University of Auckland, specializing in mechanical and mechatronics engineering, with an expected completion in 2024. Before that, he earned his Master of Engineering from China University of Mining and Technology in 2020, where he worked on advanced fault diagnosis methods and intelligent automation. His academic journey began at Yancheng Institute of Technology, where he completed his Bachelor of Engineering in 2017. His outstanding performance and research contributions have been recognized through prestigious scholarships and academic honors.

Experience 🏢

Hao Zheng is currently a Postdoctoral Researcher at The University of Hong Kong, where he is contributing to cutting-edge projects in AI-driven manufacturing and robotics. Previously, he served as a Teaching Assistant at The University of Auckland from 2023 to 2024, where he was involved in mentoring students and assisting with research-driven coursework. His experience extends beyond academia, as he has actively participated in multiple research collaborations, contributing to the development of AI-based industrial solutions.

Research Interests 🔬

Hao Zheng’s research focuses on several interdisciplinary areas at the intersection of AI and robotics. His primary interests include video understanding, particularly action recognition and segmentation, multi-modal large language models, and generative AI. He is also deeply engaged in human-robot collaboration, aiming to develop intelligent systems that enhance efficiency and safety in manufacturing. His work in human-centric manufacturing is driving innovations in AI-powered automation, making industrial processes smarter and more adaptable.

Awards 🏅

Hao Zheng has received multiple prestigious awards for his academic excellence and research contributions. He was awarded the Chinese Government Scholarship in 2020 for his outstanding research potential. In 2019, he secured the National Scholarship for Postgraduate Students and was recognized as an Excellent Student at the University of Mining and Technology. Additionally, he won the Second Prize in the May Day Mathematical Contest in Modeling, further demonstrating his analytical and problem-solving skills in engineering applications.

Publications 📚

Zheng, H., Xia, W., Xu, X. (2025). A human-robot collaborative assembly framework with quality checking based on real-time dual-hand action segmentation, Robotics and Computer-Integrated Manufacturing.

Zheng, H., Lee, R., Liang, H., Lu, Y., Xu, X. (2024). DuCAS: a knowledge-enhanced dual-hand compositional action segmentation method for human-robot collaborative assembly, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Zheng, H., Lee, R., Lu, Y., Xu, X. (2024). DuHa: a dual-hand action segmentation method for human-robot collaborative assembly, IEEE 20th International Conference on Automation Science and Engineering.

Chand, S., Zheng, H., Lu, Y. (2024). A Vision-Enabled Fatigue-Sensitive Human Digital Twin Towards Human-Centric Human-Robot Collaboration, Journal of Manufacturing Systems.

Zheng, H., Lee, R., Lu, Y. (2023). HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding, NeurIPS 2023.

Lee, R., Zheng, H., Lu, Y. (2023). Human-robot shared assembly taxonomy: A step toward seamless human-robot knowledge transfer, Robotics and Computer-Integrated Manufacturing.

Zheng, H., Chand, S., Keshvarparast, A., Battini, D., Lu, Y. (2023). Video-Based Fatigue Estimation for Human-Robot Task Allocation Optimisation, IEEE 19th International Conference on Automation Science and Engineering.

Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X., … & Bao, J. (2022). Outlook on human-centric manufacturing towards Industry 5.0, Journal of Manufacturing Systems, 62, 612-627.

Zheng, H., Cheng, G., … & Li, Y. (2021). A general fault diagnosis framework for rotating machinery and its flexible application example, Measurement, 199, 111497.

Wang, S., Zheng, H., Tang, L., … & Aw, K. (2021). Vibration-based and computer vision-aided nondestructive health condition evaluation of rail track structures, Journal of Civil Structural Health Monitoring, 1-14.

Zheng, H., Cheng, G., Li, Y., & Liu, C. (2020). A fault diagnosis method for planetary gear under multi-operating conditions based on adaptive extended bag-of-words model, Measurement, 156.

Zheng, H., Cheng, G., Li, Y., & Liu, C. (2019). A new fault diagnosis method for planetary gear based on image feature extraction and bag-of-words model, Measurement, 145, 1-13.