Yihang Tao | 陶祎航
PhD Student
City University of Hong Kong
Research Interests
- Autonomous Driving
- Spatial Intelligence
- Generative Model
- AI Security
About Me
I received my B.S. degree from the School of Information Science and Engineering, Southeast University, Nanjing, China, in 2021, and my M.S. degree from the School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, in 2024. Currently, I am pursuing my PhD degree in the Department of Computer Science at City University of Hong Kong (Advisor: Prof. Yuguang "Michael" Fang). My research broadly focuses on trustworthy Spatial Intelligence for Embodied AI, particularly in Autonomous Driving.
I am actively seeking collaborators of like-minded interests. If you are interested in my work, please feel free to contact me via email: yihang.tommy@my.cityu.edu.hk.
🚀 Research Thrust
"Imagine Embodied AI that perceives the world not just as a stream of data, but as a coherent reality. I envision endowing agents with human-like Spatial Intelligence to construct a dynamic World Model (or Digital Twin), seamlessly bridging the gap between digital simulation and physical reality."
To achieve this vision, my research aims to construct a comprehensive World Model for Spatial Intelligence, serving as the cognitive core for Embodied AI (e.g., Autonomous Vehicles). This system not only mirrors the current physical world but also reasons about its dynamics. I approach this goal through two complementary thrusts:
Collaboration for Extended 3D Understanding
This thrust focuses on extending the spatial breadth of the World Model. By establishing Collaborative Perception among agents, it overcomes individual sensing limitations (e.g., occlusions, limited range). This collective intelligence builds a more complete and robust World Model, ensuring agents maintain accurate 3D understanding even in complex, adversarial, or bandwidth-constrained environments.
Generation for Predictive 3D Understanding
This thrust focuses on extending the temporal depth of the World Model. By leveraging Generative Models and 3D Gaussian Splatting, it empowers agents to hallucinate plausible future scenarios and recover 3D geometry from sparse observations. This predictive capability enables the World Model to simulate potential outcomes, facilitating foresightful planning and decision-making for Embodied AI.
🔥 News
View All📄 Publications
(* means equal contribution)
🏆 Honors & Awards
View All💼 Academic Service
Conference Reviewer
Program Committee Member
ECCV (2026), CVPR (2026), ICASSP (2026), ACM MM (2025), ICML (2025-2026), ICLR (2025), IJCNN (2025), IEEE ICC (2025), IEEE GLOBECOM (2023-2025), IEEE ICCC (2024)
Journal Reviewer
Peer Review
IEEE TMC, IEEE TITS, IEEE TCE, Pattern Recognition, Neural Networks, EAAI, IEEE JBHI, IEEE LNET
Session Chair
IEEE GLOBECOM 2023
MWN Track, Semantic Communications Session
Teaching Assistant
City University of Hong Kong
🎓 Education
PhD @ City University of Hong Kong
Sep. 2024 - Now
Department of Computer Science
Supervisor: Prof. Yuguang "Michael" Fang
MEng @ Shanghai Jiao Tong University
Sep. 2021 - Apr. 2024
School of Computer Science
BEng @ Southeast University
Sep. 2017 - Jul. 2021
School of Information Engineering
📍 Contact
Office Address
83 Tat Chee Avenue Kowloon, Hong Kong