๐Ÿ‘‹ About Me

I am a Ph.D. student in the Department of Mechanical Engineering at Tsinghua University, supervised by Prof. Zhaoye Qin. My previous experiences include:

  • Apr 2025 - Sep 2025, Visiting scholar at Yale University working with Lu Lu.
  • Apr 2022 - Sep 2022, Research intern at AIR, Tsinghua University, supervised by Yuanchun Li.
  • Sep 2019 - Jun 2022, M.Eng. in Control Theory and Control Engineering at Soochow University (SUDA), supervised by Prof. Liang Chen and Prof. Changqing Shen.
  • Sep 2015 - Jun 2019, B.Eng. in Electrical Engineering at SUDA.

My research interests including trustworthy AI, foundation model and reliable prognostic and health management (PHM). I have published numerous papers in top-tier international journals with total google scholar citations 600+. Feel free to reach out for collaboration opportunities!

๐Ÿ”ฅ News

  • 2025.04: A paper accepted by Information fusion
  • 2025.01: A paper accepted by ADVEI
  • 2025.01: The 2024 CAST Youth Talent Support Program - PhD Special Plan - CCF
  • 2024.12: Science and Technology Award of Chinese Society of Vibration Engineering.
  • 2024.11: A paper accepted by JMS.
  • 2024.02: A paper accepted by IEEE TII.
  • 2022.05: Received the Future Scholars Scholarship from THU.

๐Ÿ“ Publications

๐Ÿ”–PHM foundation model

Information Fusion 2025
HSE: A Plug-and-Play Module for Unified Fault Diagnosis Foundation Models

[HSE: A Plug-and-Play Module for Unified Fault Diagnosis Foundation Models]
Qi Li, Bojian Chen, Qitong Chen, Xuan Li, Zhaoye Qin, Fulei Chu

  • Propose a novel Heterogeneous Signal Embedding (HSE) module that projects heterogeneous signals into a unified signal space, offering seamless integration with existing IFD architectures as a plug-and-play solution. (JCR Q1, Impact Factor: 14.4)

๐Ÿ”–Neural-symbolic Diagnosis

ADVEI 2025
Transparent information fusion network

Transparent information fusion network: An explainable network for multi-source bearing fault diagnosis via self-organized neural-symbolic nodes
Qi Li, Lichang Qin, Haifeng Xu, Qijian Lin, Zhaoye Qin, Fulei Chu

  • Introduces a transparent information fusion network with self-organized neural-symbolic nodes, enabling fully explainable multi-source fault diagnosis through knowledge-informed decision-making. (JCR Q1, Impact Factor: 8.0)
JMS 2024
Deep Expert Network

Deep Expert Network: A Unified Method toward Knowledge-Informed Fault Diagnosis via Fully Interpretable Neuro-Symbolic AI
Qi Li, Yuekai Liu, Shilin Sun, Zhaoye Qin, Fulei Chu

  • Proposes a neuro-symbolic AI approach to fault diagnosis incorporating interpretable expert knowledge using a Deep Expert Network. (JCR Q1, Impact Factor: 12.2)
IEEE TII 2024
Transparent Operator Network

Transparent Operator Network: A Fully Interpretable Network Incorporating Learnable Wavelet Operator for Intelligent Fault Diagnosis
Qi Li, Hua Li, Wenyang Hu, Shilin Sun, Zhaoye Qin, Fulei Chu

  • This work introduces an interpretable method for industrial time series classification by integrating learnable wavelet operators. (JCR Q1, Impact Factor: 12.3)

๐Ÿ”– Cross-domain Diagnosis

RESS 2023
Cross-Domain Augmentation Diagnosis

Cross-Domain Augmentation Diagnosis: An Adversarial Domain-Augmented Generalization Method for Fault Diagnosis under Unseen Working Conditions
Qi Li, Liang Chen, Lin Kong, Dong Wang, Min Xia, Changqing Shen

  • This paper presents a domain generalization approach employing adversarial learning and data augmentation for robust industrial time series classification. (JCR Q1, Impact Factor: 9.4)
IEEE TII 2022
Adversarial Domain-Invariant Generalization

Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis under Unseen Conditions
Chen Liang, Qi Li, Changqing Shen, Jun Zhu, Dong Wang, Min Xia

  • A generic domain-regressive framework for fault diagnosis using adversarial learning between feature extractors and domain classifiers, achieving robust diagnosis performance. (JCR Q1, Impact Factor: 11.7, highly cited๐ŸŒŸ)
MSSP 2021
Knowledge Mapping-Based Adversarial Domain Adaptation

Knowledge Mapping-Based Adversarial Domain Adaptation: A Novel Fault Diagnosis Method with High Generalizability under Variable Working Conditions
Qi Li, Changqing Shen, Liang Chen, Zhongkui Zhu

  • Introduces a domain adaptation strategy using adversarial learning for improved generalizability in fault diagnosis across variable conditions. (JCR Q1, Impact Factor: 7.9, highly cited๐ŸŒŸ)

๐ŸŽ– Honors and Awards

  • 2025: The 2024 CAST Youth Talent Support Program - PhD Special Plan - CCF
  • 2024: Science and Technology Award of Chinese Society of Vibration Engineering
  • 2023: Social Practice Scholarship of Tsinghua University
  • 2022: Future Scholars Scholarship of Tsinghua University
  • 2021: National Scholarship by Ministry of Education of China
  • 2021: Outstanding Student Cadre of Jiangsu Province
  • 2021: Outstanding postgraduate cadre of Soochow University
  • 2020: National Scholarship by Ministry of Education of China
  • 2020: Outstanding postgraduate of Soochow University
  • 2019: Special Award of Graduate Academic Scholarship
  • 2019: Delivered a speech at the opening ceremony of SUDA as a graduate student representative
  • 2019: Outstanding graduate of Soochow University

๐Ÿ“– Educations

  • 2025.04 - 2025.09, Visiting scholar in Statistics and Data Science, Yale University, advisor: Lu Lu.
  • 2022.09 - Present, Ph.D. in Mechanical Engineering, Tsinghua University (THU), Supervisor: Prof. Zhaoye Qin
  • 2019.09 - 2022.06, M.E. in Control Theory and Control Engineering, Soochow University (SUDA), Supervisors: Prof. Liang Chen, Prof. Changqing Shen
  • 2015.09 - 2019.06, B.E. in Electrical Engineering, Soochow University (SUDA)

๐Ÿ’ฌ Invited Talks

  • 2024.04, Beijing AI PhD Student Forum, Beijing, China (2w viewers)
  • 2023.11, PHM Conference, Hangzhou, Hangzhou, China
  • 2023.04, Tsinghua University Department of Mechanical Engineering PhD Student Forum
  • 2020.07, IEEE INDIN 2020, Warwick, UK
  • 2018.07, SDPC 2018, Xiโ€™an, China

๐Ÿ’ป Internships

  • Mar 2022 - Aug 2022, Research Intern at Institute for AI Industry Research, Tsinghua University
    • Conducted a research project focused on AI-enabled time series analysis for battery monitoring.

๐Ÿ”ง Skills

  • Language: CSC English test (104/130)
  • Programming: Python, MATLAB, PyTorch, TensorFlow
  • Hobby: Marathon, Positive Psychology, Workout

๐Ÿ–ฅ Journal Reviews

  • Information Fusion (IF)
  • Advanced Engineering Informatics (AEI)
  • IEEE Transactions on Industrial Informatics (TII)
  • Mechanical Systems and Signal Processing (MSSP)
  • IEEE Transactions on Industrial Electronics (TIE)
  • IEEE Transactions on Instrumentation and Measurement (TIM)
  • IEEE Transactions on Industrial Informatics (TII)
  • Measurement
  • Measurement Science and Technology (MST)

๐Ÿค” Project

  • Vibration noise reduction for some aircrafts (key contributor)
  • Measurement the parameters for some aircrafts (key contributor)
  • I am also leading an open-source project group called PHMBench and contribute to various PHM research initiatives.

๐ŸŽ™ Social Activities

  • 2023๏ผšVolunteer at the IFToMM International Conference on Rotordynamics
  • 2019 - 2022: Graduate monitor of the University of Mechanical and Electric Engineering, SUDA
  • 2021: Interview by Student Union of Soochow University Link
  • 2019: Delivered a speech at the opening ceremony of SUDA as a graduate student representative Link

๐Ÿ“ซ Contact

๐ŸŽˆ Myself

๐Ÿ‘“ Qi Li | Last updated: 2025.4