Ziyan Wu

I serve as VP of R&D at United Imaging Intelligence (UII America) in Boston, MA, where I lead the creation of computer vision, machine learning, and intelligent robotic systems for medical environments.

Previously, I worked as a Senior Key Expert Scientist at Siemens Corporate Research in Princeton, NJ and as a System Engineer at Honeywell Technology Solutions Labs in Shanghai, China, respectively.

I received my PhD in Computer and Systems Engineering from the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute in May 2014 under the guidance of Prof. Richard J. Radke.

Currently, I am focused on autonomizing medical devices with visual perception and building robust, fair, and data-efficient computer vision models for healthcare.

news

Jan 2026

We organized the Third Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) in conjunction with AAAI 2026.

Dec 2025

Internship We are looking for multiple research interns with computer vision and robotics background to join our Boston team for Spring and Summer 2026. Please email ziyan.wu AT uii-ai DOT com if you are interested.

Jun 2025

We organized the First Workshop on Advanced Perception for Autonomous Healthcare in conjunction with ICCV 2025.

May 2025

We organized the Sixth Workshop on Fair, Data Efficient and Trusted Computer Vision in conjunction with CVPR 2025.

Jan 2023

I delivered a talk on “Autonomizing Medical Devices with Visual Perception” at the AI4Healthcare Workshop in conjunction with FG 2023.

recent publications

MedGRPO: Multi-Task Reinforcement Learning for Heterogeneous Medical Video Understanding

Yuhao Su, Anwesa Choudhuri, Zhongpai Gao, Benjamin Planche, Van Nguyen Nguyen, Meng Zheng , Yuhan Shen, Arun Innanje , Terrence Chen, Ehsan Elhamifar , and Ziyan Wu

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

Abs Bib Paper

We introduce MedVidBench, a large-scale benchmark of 531,850 video-instruction pairs across 8 medical sources spanning video, segment, and frame-level tasks. We introduce MedGRPO, a novel RL framework for balanced multi-dataset training with cross-dataset reward normalization and a medical LLM judge that evaluates caption quality on five clinical dimensions.
@article{medgrpo2026, title = {MedGRPO: Multi-Task Reinforcement Learning for Heterogeneous Medical Video Understanding}, author = {Su, Yuhao and Choudhuri, Anwesa and Gao, Zhongpai and Planche, Benjamin and Nguyen, Van Nguyen and Zheng, Meng and Shen, Yuhan and Innanje, Arun and Chen, Terrence and Elhamifar, Ehsan and Wu, Ziyan}, year = {2026}, journal = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, }
Consistent Instance Field for Dynamic Scene Understanding

Junyi Wu, Van Nguyen Nguyen, Benjamin Planche, Jiachen Tao , Changchang Sun, Zhongpai Gao , Zhenghao Zhao, Anwesa Choudhuri , Gengyu Zhang, Meng Zheng , Feiran Wang , Terrence Chen, Yan Yan , and Ziyan Wu

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

Abs Bib Paper

We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding. Unlike prior methods that rely on discrete tracking or view-dependent features, our approach disentangles visibility from persistent object identity by modeling each space-time point with an occupancy probability and a conditional instance distribution.
@article{consistent2026, title = {Consistent Instance Field for Dynamic Scene Understanding}, author = {Wu, Junyi and Nguyen, Van Nguyen and Planche, Benjamin and Tao, Jiachen and Sun, Changchang and Gao, Zhongpai and Zhao, Zhenghao and Choudhuri, Anwesa and Zhang, Gengyu and Zheng, Meng and Wang, Feiran and Chen, Terrence and Yan, Yan and Wu, Ziyan}, year = {2026}, journal = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, }
Universal Beta Splatting

Rong Liu, Zhongpai Gao, Benjamin Planche , Meida Chen, Van Nguyen Nguyen, Meng Zheng, Anwesa Choudhuri , Terrence Chen , Yue Wang, Andrew Feng , and Ziyan Wu

International Conference on Learning Representations (ICLR), 2026

Abs Bib Paper

We introduce Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field rendering. Unlike fixed Gaussian primitives, Beta kernels enable controllable dependency modeling across spatial, angular, and temporal dimensions within a single representation.
@article{universal2026, title = {Universal Beta Splatting}, author = {Liu, Rong and Gao, Zhongpai and Planche, Benjamin and Chen, Meida and Nguyen, Van Nguyen and Zheng, Meng and Choudhuri, Anwesa and Chen, Terrence and Wang, Yue and Feng, Andrew and Wu, Ziyan}, year = {2026}, journal = {International Conference on Learning Representations (ICLR)}, }

synergistic activities

Associate Editor, IEEE Access (2017 - 2025)
Organizer, Workshop on Artificial Intelligence with Biased or Scarce Data, AAAI 2026, 2024, 2022
Organizer, Workshop on Advanced Perception for Autonomous Healthcare, ICCV 2025
Organizer, Workshop on Fair, Data Efficient and Trusted Computer Vision, CVPR 2025, 2024, 2023, 2022, 2021, 2020
Organizer, Workshop on Vision with Biased and Scarce Data, ECCV 2022, CVPR 2019, 2018
Organizer, EXPO Spotlight, CVPR 2017
Organizer, Vision Industry and Entrepreneur Workshop, CVPR 2016