Sudao HE

Sudao HE

Hong Kong University of Science and Technology

I am currently a Postdoctoral Fellow in the Department of Civil and Environmental Engineering at The Hong Kong University of Science and Technology . I am fortunate to work with Prof. Shenghan ZHANG since Sept. 2023.

I received my Ph.D. in Automation Engineering at Nanjing University of Aeronautics and Astronautics (NUAA), where I was fortunate to join the Fault Detection and Diagnostics Group led by Prof Bin JIANG (advised by Prof. Fuyang CHEN). During 2023, I was also a Postdoctral Research Associate at The Hong Kong Polytechnic University and National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), working with Prof. Yi-Qing NI. Prior to this, I obtained my bachelor’s degree from NUAA.

I have a broad research interest in leveraging data-efficient learning for advanced Structural Sensing, Inspection, and Monitoring, with an ultimate goal to develop Generalized Digital Twin Paradigms for mechanical and thermodynamic systems. My topics include Visual Measurement, Multi-modal Learning, Domain Adaptation / Generalization, Zero-shot / Few-shot Learning, Data Stream Mining, Incremental Learning, Label-noise Learning and Federated Learning.

I have published several papers at the top journals such as IEEE Transactions on Neural Networks and Learning Systems, Reliability Engineering & System Safety and IEEE Transactions on Instrumentation and Measurement.

If you are seeking any form of academic cooperation, please feel free to email me at sudao.he@ust.hk.

🔥 News

  • 2025.09: 😁 I will chair a special session at EWSHM 2026 alongside Dr. Hanqing Zhang, Prof. Shenghan Zhang, Dr. Alice Cicirello, and Prof. Hae Young Noh. We warmly invite you to join us in Toulouse, France.
    (SS 11: AI-powered structural sensing and health monitoring for civil engineering structures)
  • 2025.08: 🎉 I am honored to serve as a member of the Technical Committee for ICMIE 2025. Welcome to join us in Tai Cang (China).
  • 2025.06: 🎊 Prof Shenghan Zhang presented our recent work UHGAN for bottom-up structure-level damage assessment on ICOSSAR’25.
  • 2025.06: 🎊 I present my recent work DMPN for trustworthy structural damage assessment by fusing heterogeneous data on ICOSSAR’25.
  • 2025.03: 🎉 Physical-Constrained FiberNet on strain distributions interpretation, collaborated with Xuanyi Lu and Shenghan Zhang, has been published in Automation in Construction.
  • 2025.02: 🎉 One work collaborated with Tongji University on steel fatigue cracks detection using geometric computer vision technology has been published in Structural Control and Health Monitoring.
  • 2025.01: 😁 I have been honored with the title of Outstanding Reviewer for IEEE TIM.
  • 2024.12: 🎉 Weakly-aligned Cross-modal Learning Framework for subsurface defect segmentation on building façades has been accepted for publication in Automation in Construction.
  • 2024.09: 📖 I am honored to give a guest lecture again on Machine Learning in Structural Sensing and Health Monitoring.
  • 2024.06: 😁 I will exhibit my work with Prof. Zhang, Dr. Mishra and Prof. Yuen on subsurface defect segmentation in EWSHM 2024.
  • 2024.06: 🎊 I will serve as Associate Editor for IEEE Transactions on Instrumentation and Measurement.
  • 2024.02: 🎉 My paper collaborated with Dr. AO Wai Kei and Prof. NI Yi-Qing has been accepted for publication in IEEE TIM.
  • 2023.09: 📖 I give a guest lecture on Machine Learning in Structural Sensing and Health Monitoring.
  • 2023.09: 🚀 I join the group of Prof. Shenghan ZHANG at The Hong Kong University of Science and Technology.

📝 Selected Research Papers

🔶 ML with Incomplete Data

IEEE TNNLS
sym

Sudao He, Fuyang Chen, Hongtian Chen. A latent representation generalizing network for domain generalization in cross-scenario monitoring, IEEE Transactions on Neural Networks and Learning Systems. (JCR Q1, IF: 10.4) (cite)

  • LRGN: A framework for Cross-scenario Monitoring without data of concerned events (such as faults, defect and physical intrusion) in a new scenario.
  • Academic Impact: Efficient domain generalization with agnostic embedding space by estimating domain shifts of vibration signals by a sequential- variational generative adversarial network.
  • Industry Impact: Verification based on real-field data from distributed optical fiber sensors in Jinliwen line and Nanjing Metro Line S7.
IEEE TIM
sym

Sudao He, Fuyang Chen, Ning Xu, Hongtian Chen. Online monitoring for non-stationary operation via a collaborative neural network. IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-12, 2022. (JCR Q1, IF: 5.6)(cite)

  • CoNN: A Collaborative Neural Network for non-stationary operation monitoring through online adaptation with theoretical guaranteed convergence.
  • Academic Impact: Concept-sharing feature layers for adaptation to prior knowledge changes and concept-exclusive classification layers to learn concept-specific posterior knowledge.
  • Industry Impact: CoNN is verified based on actual data from Nanjing Metro Line S7.
Neurocomputing
sym

Sudao He, Fuyang Chen, Bin Jiang. Physical intrusion monitoring via local-global network and deep isolation forest based on heterogeneous signals. Neurocomputing, vol. 441, pp. 25-35, 2021 (JCR Q2, IF: 6)(cite)

  • We propose a local–global semi-sharing network for heterogeneous signals in physical intrusion monitoring
  • Academic Impact: A deep isolation forest is developed to learn from high-dimensional and extremely-imbalanced data.
  • Industry Impact: An F1 score of 0.780 under a class ratio of 83.3 is achieved based on real-field data from Nanjing Metro Line S7.

🚩 ML with Unreliable Data

IEEE TIM
sym

Sudao He, Wai Kei Ao, Yi-Qing Ni. A Unified Label Noise-Tolerant Framework of Deep Learning-based Fault Diagnosis via A Bounded Neural Network, IEEE Transactions on Instrumentation and Measurement, 2024. (JCR Q1, IF: 5.6)(code)

  • A unified framework for label-noise fault diagnosis of mechanical system.
  • Academic Impact: Theoretical guarantee and enhancement of label noise tolerance.
  • Industry Impact:
    • Applicable to different deep learning models.
    • Implementation of multiple variants and SOTA methods.
    • Real-field tests on automatic train control antenna beam of high-speed train.
IEEE Sensors Journal
sym

Fuyang Chen, Sudao He, Yiwei Li, Hongtian Chen. Data-driven monitoring for distributed sensor networks: an end-to-end strategy based on collaborative learning, IEEE Sensors Journal, vol. 22, no. 22, pp. 21795-21805, 15 Nov.15, 2022. (JCR Q1, IF: 4.3)(cite)

  • A collaborative soft-label network for label-noise learning in distributed sensor networks.
  • Academic Impact: Introduce local similarities for modifying hard samples through a dual-space smoothing technique.
  • Industry Impact: An accuracy of 88.33 is achieved when 40% of the labels are not correct using real-field data from Nanjing Metro Line S7. It can be extended to an unsupervised learning task.

💻 Publications

My full paper list is shown at my Google Scholar. Different research topics are highlighted using 🔶 Sensing, 🚩 Inspection, 🚄 Monitoring

First-author Papers

🔶🚩J6. Sudao He, Gang ZHAO, Jun CHEN, Shenghan ZHANG, Dhanada MISHRA, Matthew Ming-Fai YUEN. Weakly-Aligned Cross-Modal Learning Framework for Subsurface Defect Segmentation on Building Facades Using Unmanned Aerial Vehicles. Automation in Construction. (JCR Q1, IF: 9.6)(cite)(code 1)(code 2)(toolbox)

🚄J5. Sudao He, Wai Kei Ao, Yi-Qing Ni. A Unified Label Noise-Tolerant Framework of Deep Learning-based Fault Diagnosis via A Bounded Neural Network, IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-15, 2024, Art no. 3513515. (JCR Q1, IF: 5.6)(cite)(code)

🚄J4. Sudao He, Fuyang Chen, Hongtian Chen. A latent representation generalizing network for domain generalization in cross-scenario monitoring, IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 11, pp. 16644-16658, 2024. (JCR Q1, IF: 10.4) (cite)

🚄J3. Sudao He, Fuyang Chen, Ning Xu. High-speed train localization algorithm via cooperative multi-classifier network using distributed heterogeneous signals, Journal of the Franklin Institute, 2023, 360(12): 8096-8117. (JCR Q1, IF: 4.1)(cite)

🚄J2. Sudao He, Fuyang Chen, Ning Xu, Hongtian Chen. Online monitoring for non-stationary operation via a collaborative neural network. IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-12, 2022. (JCR Q1, IF: 5.6) (cite)

🚄J1. Sudao He, Fuyang Chen, Bin Jiang. Physical intrusion monitoring via local-global network and deep isolation forest based on heterogeneous signals. Neurocomputing, vol. 441, pp. 25-35, 2021 (JCR Q1, IF: 5.5)(cite)

Papers as second and other authors

🔶J4. Xuanyi Lu, Sudao He, Shenghan Zhang. Deep learning for automated crack quantification with distributed fiber optic sensing: Addressing strain overlap and interface nonlinearity. Automation in Construction, Volume 176, 2025, 106280. (JCR Q1, IF: 9.6) (cite)

🚄J3. Qian-Qian Yu, Jie Wang, Xiang-Lin Gu, Sudao He, Shenghan Zhang. An Attention-Based Detection Method of Fatigue Cracks on Steel. Structural Control and Health Monitoring, 2025, 7487687, 21 pages. (JCR Q1, IF: 4.6) (cite)

🚄J2. Wentao Liu, Sudao He, Jianpeng Mou, Ting Xue, Hongtian Chen, Weili Xiong. Digital twins-based process monitoring for wastewater treatment processes. Reliability Engineering & System Safety, 2023: 109416. (JCR Q1,IF: 8.1)(cite)

🚄J1. Fuyang Chen, Sudao He, Yiwei Li, Hongtian Chen. Data-driven monitoring for distributed sensor networks: an end-to-end strategy based on collaborative learning, IEEE Sensors Journal, vol. 22, no. 22, pp. 21795-21805, 15 Nov.15, 2022. (JCR Q1, IF: 4.3)(cite)

Conference

🚩C3. Hanpeng Wang, Sudao He, Shenghan Zhang. An uncertainty-aware graph attention network for structural damage assessment leveraging heterogeneous information. Proceedings of 14th International Conference on Structural Safety and Reliability (ICOSSAR’25), June 1-6, 2025 in Los Angeles, CA, USA.

🚩C2. Sudao He, Hanpeng Wang, Shenghan Zhang. Trustworthy structural damage assessment using heterogeneous measurements. Proceedings of 14th International Conference on Structural Safety and Reliability (ICOSSAR’25), June 1-6, 2025 in Los Angeles, CA, USA.

🚩C1. Sudao HE, Shenghan ZHANG, Dhanada MISHRA, Matthew Ming-Fai YUEN. A graph learning approach for automatic subsurface defects segmentation in building façade using RGB and infrared images. Proceedings of the 10th European Workshop on Structural Health Monitoring (EWSHM 2024), June 10-13, 2024 in Potsdam, Germany. e-Journal of Nondestructive Testing. (cite)

🎖 Honors and Awards

  • 2025.01 Outstanding Reviewer of IEEE Transactions on Instrumentation and Measurement.
  • 2024.01 Outstanding Reviewer of IEEE Transactions on Instrumentation and Measurement.
  • 2021.10 Social Practice Outstanding Individual
  • 2020.10 Merit Postgraduate
  • 2019.10 Advanced Individual in Scientific Research and Innovation
  • 2016.09 NUAA Scholarship (1st Class)

📖 Educations

  • 2016.09 - 2022.10, Ph.D., College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing.
  • 2012.09 - 2016.06, Undergraduate, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing.

💬 Invited Talks

ML in SSHM
sym
  • 2024.10, Machine Learning in Structural Sensing and Health Monitoring
  • 2023.09, Machine Learning in Structural Sensing and Health Monitoring

🤝 Experience

  • 2023.03 - 2023.08, Postdoctoral Research Associate at National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch), The Hong Kong Polytechnic University, Hong Kong SAR.

  • Till now, I serve as a reviewer in many journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Instrumentation and Measurement, Journal of Vibration and Control, IEEE/CAA Journal of Automatica Sinica and ISA Transcations.

🌎 Service

Journal Editorship

Journal Reviewer

  • Journal of Computing in Civil Engineering
  • Journal of Vibration and Control
  • ISA Transactions
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Automation Science and Engineering
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Instrumentation and Measurement
  • IEEE/CAA Journal of Automatica Sinica