Jieping Ye
Papers
2
Total Citations
82
H-Index
2
About
Jieping Ye is a leading figure in machine learning and artificial intelligence, with a particular focus on advancing few-shot learning and embodied intelligence. His work addresses the critical challenge of enabling machines to learn effectively from minimal data, as highlighted in his highly cited survey, “A survey on machine learning from few samples” (2023, 80 citations), which has become a foundational resource for researchers tackling data scarcity in AI. More recently, Ye has pushed the boundaries of embodied AI through his innovative work on grounding 3D object affordance (2025), a task that links perception and action by enabling intelligent robots to locate and manipulate objects in three-dimensional space based on human language instructions and visual observations. This research bridges the gap between abstract commands and physical interaction, a crucial step toward practical robotics. With a career marked by impactful contributions that span from theoretical frameworks to real-world applications, Jieping Ye continues to shape the future of AI, inspiring students and researchers to explore how machines can learn, perceive, and act in complex environments.
Research Focus
Key Achievements
Top Papers
- 1A survey on machine learning from few samples80 citations · 2023
- 2