Jieneng Chen

Pronounce: jee-eh-nung  ·  陈杰能  ·  Email: jienengc [at] stanford.edu

I am a postdoctoral researcher at Stanford SVL. I received my PhD in Computer Science at Johns Hopkins University in 2026, advised by Alan Yuille.

I study intelligence in the physical world by structuring raw observations into spatial code. My works laid the foundation, spanning vision encoders and world models. I am best known for TransUNet, which unifies local and global context and has over 10,000 citations.

I am honored as a Young American Scientist by the pretigious magazine Scientific American, a Siebel Scholar in bioengineering, and a recipient of a few best paper awards.

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News

• 07/2026. I am deeply honored to be recognized by Scientific American as one of the Young American Scientists. The magazine has inspired my curiosity about science since childhood.
• 06/2026. Co-organized WMAS (World Model Meets Active Sensing) and GCV (Generative Computer Vision) workshops at IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) 2026.
• 06/2026. Invited to serve as Area Chair for the 41st AAAI Conference on Artificial Intelligence (AAAI-27).
• 04/2026. World-in-World is selected as Oral presentation (1%) in ICLR 2026.
• Happy to chat if you’re looking for research collaboration or research opportunities.

Research

When we watch a ball bounce, we do more than recognize pixels. We infer 3D structure, anticipate what may happen next, and reason about physical interactions over time. What representation could give machines the same ability to understand and act in a dynamic world?

I seek fundamental representations of the physical-world observations. My work builds increasingly rich world priors through learned multimodal representations (Chen ’21, ’24, ’26) and generative representations (Lu ’25; Chen ’26). This research path aims to enable machines to reason, plan and adapt through closed-loop interaction (Zhang ’26).

Beyond representations, I apply these ideas to robots operating in complex environments and to cancer diagnosis and treatment, with the long-term goal of improving the lives of patients and their families.

Publications from the past two years. Full list on Google Scholar.

Research Highlights

ICLR'26 Oral

World-in-World

Closed-loop world model — generation, perception & action in the physical world.

ICLR'25

GenEx

Turn a single image into an explorable 3D world. Agents navigate generated environments.

ICCV'25

MeWM

Medical World Model — generative tumor evolution simulation for personalised treatment planning.

ICLR'26 Oral

World-in-World

Closed-loop world model — generation, perception & action in the physical world.

ICLR '25

GenEx

Turn a single image into an explorable 3D world. Agents navigate generated environments.

ICCV '25

MeWM

Medical World Model — generative tumor evolution simulation for personalised treatment planning.

WACV '26

4D-Animal

Freely reconstructing animatable 3D animals from monocular video.

	World-in-World: World Models in a Closed-Loop World Jiahan Zhang*, Muqing Jiang*, Nanru Dai, TaiMing Lu, Arda Uzunoglu, Shunchi Zhang, Yana Wei, Jiahao Wang, Vishal Patel, Paul Liang, Daniel Khashabi, Cheng Peng, Rama Chellappa, Tianmin Shu, Alan Yuille, Yilun Du, Jieneng Chen. ICLR, 2026. Oral (top 1%). World models live and die by their closed-loop success, not flawless generated visuals. Paper | OpenReview | Project | Leaderboard | Demo | Code
	Fast Generative DeOcclusion for Visual Geometry and Robotics Jieneng Chen*, Tiezheng Zhang*, Xiwei Xuan, Ju He, Yifan Yin, Haojun Shi, Suyu Ye, Xinyi Li, Ruisheng Yuan, Tianmin Shu, Alan Yuille. CVPR, Findings, 2026
	GenEx: Generating an Explorable World TaiMing Lu, Tianmin Shu, Alan Yuille, Daniel Khashabi, Jieneng Chen. ICLR, 2025. Turn a single image into a 3D world adventure. Embodied agents refine beliefs by predicting unseen parts of the physical world. JHU News | Paper | Blog | Project | Code
	Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning Yijun Yang, Zhao-Yang Wang, Qiuping Liu, Shuwen Sun, Kang Wang, Rama Chellappa, Zongwei Zhou, Alan Yuille, Lei Zhu, Yu-Dong Zhang, Jieneng Chen. ICCV, 2025. Precision medicine via generative world modeling. Paper | Code | Project
	Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models Xingrui Wang, Wufei Ma, Tiezheng Zhang, Celso Miguel de Melo, Jieneng Chen†, Alan Yuille†. CVPR, 2025. Highlight. Paper | Code | HuggingFace
	SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models Wufei Ma, Luoxin Ye, Nessa McWeeney, Celso Miguel de Melo, Alan Yuille, Jieneng Chen. CVPR, 2025. Highlight. Paper
	4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos Shanshan Zhong, Jiawei Peng, Zehan Zheng, Zhongzhan Huang, Wufei Ma, Guofeng Zhang, Qihao Liu, Alan Yuille, Jieneng Chen. WACV, 2026. Paper | Code
	ViTamin: Designing Scalable Vision Models in the Vision-Language Era Jieneng Chen, Qihang Yu, Xiaohui Shen, Alan Yuille, Liang-Chieh Chen. CVPR, 2024. First vision-centric encoder design for LMMs; SoTA on 60+ benchmarks in 2024. Paper | Code | HuggingFace | timm | open_clip
	LLaVolta: Efficient Large Multi-modal Models via Visual Context Compression Jieneng Chen, Luoxin Ye, Ju He, Zhaoyang Wang, Daniel Khashabi, Alan Yuille. NeurIPS, 2024. Paper | Code | Project
	TransUNet: Rethinking the U-Net Architecture Design for Medical Image Segmentation through the Lens of Transformers Jieneng Chen, Jieru Mei, Xianhang Li, Yongyi Lu, Qihang Yu, Qingyue Wei, Xiangde Luo, Yutong Xie, Ehsan Adeli, Yan Wang, Matthew P Lungren, Shaoting Zhang, Lei Xing, Le Lu, Alan Yuille, Yuyin Zhou. Medical Image Analysis, Oct 2024. Top-15 cited 2021 paper in all AI fields. Most downloaded on ScienceDirect. Most cited in MedIA. ICML-W 2021 | Journal | Code

Feature interview, Scientific American — Young American Scientists 2026

• Invited talk at NSF IAIFI on physics & AI, Boston.
• Lab seminar at Stanford.
• Vision seminar at UIUC.
• Guest lecture at Rice University.
• Lab seminars at Harvard / MIT / HMS / MGH / Dana-Farber.
• Talk at ICLR 2025 Workshop on Embodied Intelligence with LLMs in Open City Environment (slides).
• Talks at JHU: ChemBE, Cognitive Science, CLSP, MINDS, AIEM.

• Instructor: Machine Imagination (EN.601.208), JHU, 2025 & 2026.

• Reviewer: AAAI, CogSci, CVPR, ECCV, ICCV, ICLR, ICML (Reviewer Award), MedIA, MICCAI, Nature Machine Intelligence, NeurIPS, TAPMI, TMI
• Co-organizer: CVPR-GCV 2025/2026, CVPR-WMAS 2026, CVPR-CV4Clinical 2026, ICCV-ESR 2025.
• Area Chair: AAAI 2027.
• Inclusion: Lecture for JHU WSE Pre-College Program 2025. JHU CS mentor hours.

I am fortunate to have collaborated with talented students at JHU.

• TaiMing Lu, JHU Undergraduate → Princeton CS PhD
  1 publication on GenEx. Michael J. Muuss Research Award; finalist for CRA Outstanding Undergraduate Researcher Award.
• Shanshan Zhong, SYSU MS → CMU LTI PhD
  1 publication on 4D-Animal.

My doctoral research was made possible through the generous support of ARL, IARPA, NSF, NIH, ONR, Lambda, NVIDIA, Google Cloud, JHU, Stanford, Harvard, the Biswas Foundation, the Siebel Foundation, the Patrick J. McGovern Foundation, and the Lustgarten Foundation.