About

I am a Senior Applied Research Scientist at NVIDIA, working on deep learning methods for medical image analysis. My research focuses on developing AI-driven solutions to advance clinical workflows, spanning medical image segmentation, neural architecture search, federated learning, and generative models for medical imaging.

I received my Ph.D. in Computer Science from Rutgers University in 2019, advised by Prof. Dimitris Metaxas. Prior to that, I obtained my M.Phil. in Mechanical Engineering from HKUST and my B.Eng. from Tsinghua University.

I am a core contributor to MONAI (Medical Open Network for Artificial Intelligence) and NVIDIA FLARE (Federated Learning Application Runtime Environment).

Research Interests

• Vision-Language Models for Healthcare
• Generative Models for Medical Image Synthesis
• Federated Learning and Privacy-Preserving AI
• Self-supervised and Semi-supervised Learning
• Neural Architecture Search for Medical Imaging
• Medical Image Segmentation and Analysis

News

• [2026] Cosmos 3 released on arXiv 2026.
• [2026] AutoMedBench released on arXiv 2026.
• [2026] MAISI-v2 accepted at AAAI 2026.
• [2026] OmniVinci accepted at ICLR 2026 (Poster).
• [2025] Better Tokens for Better 3D accepted at NeurIPS 2025 (Poster).
• [2024] FedBPT accepted at ICML 2024 (Poster).
• [2024] Disruptive Autoencoders accepted at MIDL 2024 (Oral).
• [2023] SwinUNETR-V2 accepted at MICCAI 2023.

Selected Publications

(See full list on Google Scholar or the Publications page)

Cosmos 3: Omnimodal World Models for Physical AI
N. Agarwal, A. Ali, J. Allen, …, D. Yang, et al.
Tech report, 2026
paper code project

AutoMedBench: Towards Medical AutoResearch with Agentic AI Models
J. Liu, S. Song, Y. Wang, J. Mao, H. Chen, X. Huang, T. Qi, P. Guo, Y. Tang, Y. He, C. Zhao, A. Myronenko, D. Yang, D. Xu, Y. Zhou
Tech report, 2026
paper code

MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss
C. Zhao, P. Guo, D. Yang, Y. Tang, Y. He, B. Simon, M. Belue, S. Harmon, B. Turkbey, D. Xu
AAAI, 2026
paper

OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM
H. Ye, C.-H.H. Yang, A. Goel, W. Huang, Z. Wan, …, D. Yang, et al.
ICLR, 2026
paper

Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging
I.E. Hamamci, S. Er, S. Shit, H. Reynaud, D. Yang, P. Guo, M. Edgar, D. Xu, B. Kainz, B. Menze
NeurIPS, 2025
paper

FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
J. Sun, Z. Xu, H. Yin, D. Yang, D. Xu, Y. Liu, Z. Du, Y. Chen, H.R. Roth
ICML, 2024
paper

VISTA3D: Versatile Imaging SegmenTation and Annotation Model for 3D Computed Tomography
Y. He, P. Guo, Y. Tang, A. Myronenko, V. Nath, Z. Xu, D. Yang, C. Zhao, …, D. Xu, W. Li
Tech report, 2024
paper

SwinUNETR-V2: Stronger Swin Transformers with Stagewise Convolutions for 3D Medical Image Segmentation
Y. He, V. Nath, D. Yang, Y. Tang, A. Myronenko, D. Xu
MICCAI, 2023
paper

UNETR: Transformers for 3D Medical Image Segmentation
A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H.R. Roth, D. Xu
WACV, 2022
paper code

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H.R. Roth, D. Xu
MICCAI BrainLes Workshop, 2021
paper code

MONAI: An Open-Source Framework for Deep Learning in Healthcare
M.J. Cardoso, W. Li, R. Brown, …, D. Yang, et al.
Tech report, 2022
paper code

Self-supervised Pre-training of Swin Transformers for 3D Medical Image Analysis
Y. Tang, D. Yang, W. Li, H.R. Roth, B. Landman, D. Xu, V. Nath, A. Hatamizadeh
CVPR, 2022
paper code

NVIDIA FLARE: Federated Learning from Simulation to Real-World
H.R. Roth, Y. Cheng, Y. Wen, I. Yang, Z. Xu, …, D. Yang, et al.
Tech report, 2022
paper code

DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Y. He, D. Yang, H. Roth, C. Zhao, D. Xu
CVPR, 2021
paper

Federated Semi-supervised Learning for COVID Region Segmentation in Chest CT Using Multi-national Data
D. Yang, Z. Xu, W. Li, A. Myronenko, H.R. Roth, S. Harmon, et al.
Medical Image Analysis, 2021
paper

Artificial Intelligence for the Detection of COVID-19 Pneumonia on Chest CT Using Multinational Datasets
S.A. Harmon, T.H. Sanford, S. Xu, …, D. Yang, et al.
Nature Communications, 2020
paper

C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
Q. Yu, D. Yang, H. Roth, Y. Bai, Y. Zhang, A.L. Yuille, D. Xu
CVPR, 2020
paper

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
D. Yang, D. Xu, S.K. Zhou, B. Georgescu, M. Chen, S. Grbic, D. Metaxas, D. Comaniciu
MICCAI, 2017
paper