Ruyi Wang
Ruyi Wang (王濡翼)
AI4Science & Hydrogen Energy & Embodied AI, Tongji University
975967579 [at] qq.com  /  2333059 [at] tongji.edu.cn

About

"Everyone is Faker.YOU TOO.ME TOO."

I am Ruyi Wang (王濡翼), a researcher dedicated to rescuing the PEM water electrolysis system from the invisible threat of ion contamination.

I was academically born and raised at Tongji University, where I obtained both my Bachelor's and Master's degrees from the School of Automotive Studies. Currently, I conduct AI4Science-driven research on fault diagnosis and physics-informed modeling for PEM fuel cells and PEM water electrolyzers under the supervision of Prof. Xuezhe Wei and Prof. Jiangong Zhu.

My current projects span from ion contamination diagnosis with multi-modal voltage & impedance data, to physics-informed Transformers, and to embodied AI / legged & humanoid robots for loco-manipulation. If you are interested in collaboration, feel free to reach out via email.

Research Interests

  • PEM water electrolysis & hydrogen energy
    Degradation mechanisms, ion contamination, stack modeling and monitoring.
  • Data-driven & physics-informed fault diagnosis
    Sliding-window Transformers, electrochemical impedance spectroscopy, hybrid rule + learning frameworks.
  • AI4Science
    Scientific machine learning, surrogate modeling, uncertainty-aware prediction for energy systems.
  • Embodied AI & humanoid / legged robots
    Motion retargeting, RL/IL for loco-manipulation, whole-body evaluation metrics (ZMP, COM, foot contact).

Education

Tongji University, Shanghai, China
  • M.Eng in Automotive Engineering, School of Automotive Studies
    Sep 2023 – Jul 2026 (expected)
  • B.Eng in Automotive Engineering, School of Automotive Studies
    Sep 2018 – Jun 2023

Experiences

2023.09 – 2026.07 (expected)
M.Eng Student & Researcher @ School of Automotive Studies, Tongji University
AI4Science for PEM fuel cells and PEM water electrolyzers.
Focus on ion contamination diagnosis, physics-informed modeling, and multi-modal data fusion.
Instructors: Xuezhe Wei, Jiangong Zhu, Xiaoping Wu
2025.09 – 2026.01
Robotics Locomotion & Manipulation Algorithm Intern @ Agibot (智元机器人), Shenzhen, China
Project: Whole-body Loco-manipulation (Sim2Real)
• Debugged AIMRT–ROS SDK communication for full-stack HW/SW integration.
• Developed dynamics-constrained motions with Crocoddyl + Pinocchio for dataset expansion.
• Built retargeting evaluation methods based on GMR & PHUMA.
• Developed VR teleoperation whole-body controller for Agibot Lingxi X2; validated Sim2Sim & Sim2Real.
Instructor: Shaohe Du
2025.04 – 2025.09
Embodied Locomotion Control Algorithm Intern @ Xiaomi Robotics, Beijing, China
Project: Biped Locomotion with Toe Modeling
• Investigated functional roles of toe DoF in biped locomotion; validated with Cassie-style simulation for stability & gait diversity.
• Built motion data processing & visualization pipeline; converted retargeted mocap into RL-ready trajectories for PPO training.
• Integrated imitation learning methods (AMP, OMNIH2O) in Legged Gym; refactored DataLoader to support custom trajectory formats.
• Trained and debugged PPO locomotion policies in Isaac Gym; optimized rewards, contact forces, and PPO hyperparameters.
Instructor: Jianhua Feng
2025.01 – 2025.03
Embodied Perception Algorithm Intern @ Anker Innovations (安克创新), Shanghai, China
Project: 3D Simulation Asset Generation & Multi-engine Learning Survey
• Surveyed heterogeneous simulation/rendering stacks: Gazebo, MuJoCo, Isaac Sim; Unreal/Unity; ROS2/LCM; NeRF & 3D/4D Gaussian Splatting.
• Implemented ROS2 control interfaces in Isaac Sim for robot arm control, sensor publishing, and command streaming.
• Built an end-to-end pipeline: image capture → 3DGS reconstruction → point cloud editing → foreground/background separation → sim scene generation.
Instructor: Minyi Chen
2024.03 – 2024.10
ADAS Functional Test & Automation Intern @ NIO (蔚来), Shanghai, China
Project: Regulatory Scenario & State-machine Transition Automated Testing for Production ADAS
• Deployed WorldSim and communication modules (DDS, feature_app) on Ubuntu to support automated ADAS testing.
• Modeled regulatory scenarios and authored test cases for state-machine transitions and boundary conditions across AEB/BSD/LCA/SDOW, ACC/NP(NOP)/AHB/ALC, and parking features.
• Developed automation scripts for release → run → result collection → report generation; improved efficiency and reproducibility.
• Performed log replay (LogSim) for regression testing and bug localization.
Instructor: Xinghai Huang
2023.04 – 2023.08
Data Communication Intern @ Porsche Engineering, Shanghai, China
Project: ADAS Simulation Scenarios & Data Communication Pipeline
• Built a dual-system co-simulation stack (Windows + WSL Ubuntu) with CARLA + ROS for cross-platform communication and integration testing.
• Reconstructed real-world Jiading (Shanghai) road segments in CARLA; implemented typical FCW/SCW scenarios (hard brake, cut-in, etc.).
• Designed a multi-node middleware pipeline: CARLA sensors → ROS perception → decision logic → socket → Windows QT dashboard visualization.
• Developed UI regression automation with Behave + Selenium; built QT/QML dashboard for speed & warning indicators.
Instructor: Mojun Qian
2018.09 – 2023.06
B.Eng Student @ School of Automotive Studies, Tongji University
Major in Automotive Engineering, with early research in hydrogen energy and fuel cell systems.
Instructor: Haifeng Dai,

Publications ( / )

Ruyi Wang, Co-authors (to be updated)
In preparation, 2025 (to be updated)
Ruyi Wang, Jiangong Zhu, Haoyu Zhang, Chao Wang, Xiaoping Wu, Ming Wu, Jianqiang Xu, Hao Yuan, Wei Liu, Haifeng Dai & Xuezhe Wei
Discover Applied Sciences, 2025
[Title to be updated]
Ruyi Wang, Co-authors (to be updated)
[Venue / Year to be updated]

GitHub

Physics-informed Transformer for ion contamination diagnosis in PEM water electrolyzers (SWC-PC-T-ID framework).
Python
A retargeting evaluation toolkit: ZMP/COM analysis, foot contact visualization, and interactive 3D inspection for humanoid robots.
Python

Selected Honors & Awards

  • (to be updated)
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