Jieneng Chen
Kempner Institute Research Fellow
About
Jieneng Chen is a Ph.D. candidate in Computer Science at Johns Hopkins University. His research focuses on spatial and physical intelligence, building foundation models that transform raw visual input into structured, geometry-aware representations for reasoning in 3D and 4D environments. He is best known for TransUNet, a widely adopted architecture. He is advancing world models and spatial-code frameworks for embodied AI. His work spans computer vision, multimodal learning, robotics, and medical AI, aiming to give machines predictive, intuitive understanding of the physical world. He is a Siebel Scholar.
Research Focus
Jieneng builds foundation models for spatial and physical intelligence. Jieneng’s work turns raw visual input into structured, geometry-aware representations that support reasoning, imagination, and action in 3D/4D environments. Spanning vision, multimodal learning, embodied AI, and medical imaging, Jieneng aims to give machines an intuitive and predictive understanding of the physical world for real-world impact.