⭐About Me and IEMCS-Lab

I work at Beihang University as an Associate Professor now in Beijing, China. Our group is focused on Intelligent Electromagnetic Computing and Sensing (IEMCS). Here is the photo of all members (2025) in our IEMCS-Lab:

IEMCS-Lab’s and my research interests lie in computational electromagnetics, particularly AI-based computational electromagnetics and the application of deep learning in fast and intelligent electromagnetic modeling and inversion imaging algorithms. This includes electromagnetic simulation of aircraft, large-scale parallel computing, array antenna optimization, electromagnetic inverse scattering imaging, and biomedical imaging.

I am currently a master’s supervisor, recruiting 1–2 master’s students each year. If you are interested in joining our team, please email me at taoshan@buaa.edu.cn.

📝 Publications

✒️ Journal Paper

First/Corresponding-author Publications

• I-J-9 Shan Tao, Li Maokun, Yang Fan, Xu Shenheng and Su Donglin, Complex-valued Neural Operator for Solving 2D Wave Equation Based on Graph Neural Network, IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2025.3609818.
• I-J-8 Shan Tao, Li Maokun, Yang Fan and Xu Shenheng, Solving Combined Field Integral Equations with Physics-informed Graph Residual Learning for EM Scattering of 3D PEC Targets, IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2023.3331262.
• I-J-7 Shan Tao, Zen Jinhong, Song Xiaoqian, Li Maokun, et al., Physics-informed Supervised Residual Learning for Electromagnetic Modeling, IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2023.3245281.
• I-J-6 Shan Tao, Lin Zhichao, Song Xiaoqian, Li Maokun, et al., Physics-informed Supervised Residual Learning for 2D Inverse Scattering Problem, IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2023.3242372.
• I-J-5 Shan Tao, Lin Zhichao, Song Xiaoqian, Li Maokun, et al., Neural Born Iterative Method For Solving Inverse Scattering Problems: 2D Cases, IEEE Transactions on Antennas and Propagation, 2022, doi: 10.1109/TAP.2022.3217333.
• I-J-4 Shan Tao, Guo Rui, Li Maokun, et al., Application of Multitask Learning for 2-D Modeling of Magnetotelluric Surveys: TE Case, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-9, 2022, Art no. 4503709, doi: 10.1109/TGRS.2021.3101119.
• I-J-3 Shan Tao, Pan Xiaotian, Li Maokun, et al., Coding programmable metasurfaces based on deep learning techniques, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 10, no. 1, pp. 114-125, March 2020, doi: 10.1109/JETCAS.2020.2972764.
• I-J-2 Shan Tao, Li Maokun, et al., Phase Synthesis of Beam-Scanning Reflectarray Antenna Based on Deep Learning Technique, Progress In Electromagnetics Research, Vol. 172, 41-49, 2021, doi:10.2528/PIER21091307.
• I-J-1 Shan Tao, Tang Wei, Dang Xunwang, Li Maokun, et al., Study on a fast solver for Poisson’s equation based on deep learning technique, IEEE Transactions on Antennas and Propagation, vol. 68, no. 9, pp. 6725-6733, Sept. 2020, doi: 10.1109/TAP.2020.2985172.

Co-Authored Publications

• C-J-6 Liu Daoqi, Shan Tao, Li Maokun, Yang Fan and Xu Shenheng, Multi-Frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems, IEEE Transactions on Computational Imaging, vol. 11, pp. 1243-1257, 2025, doi: 10.1109/TCI.2025.3607150.
• C-J-5 Cao Huilin; Sun Haoran, Liang Jingwei, Shan Tao, Ren Yuxiang and Maokun Li, Physics-Data Hybrid Driven Edge-Featured Graph Attention Networks for Surface Currents Learning of 3D PEC Targets, IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2025.3602467.
• C-J-4 Salucci M, Arrebola M, Shan Tao and Li M, Artificial Intelligence: New Frontiers in Real-Time Inverse Scattering and Electromagnetic Imaging, IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6349-6364, Aug. 2022, doi: 10.1109/TAP.2022.3177556.
• C-J-3 Guo R, Shan Tao et al., Physics Embedded Deep Neural Network for Solving Volume Integral Equation: 2-D Case, in IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6135-6147, Aug. 2022, doi: 10.1109/TAP.2021.3070152.
• C-J-2 Guo R, Lin Z, Shan Tao, et al., Physics Embedded Deep Neural Network for Solving Full-Wave Inverse Scattering Problems, in IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6148-6159, Aug. 2022, doi: 10.1109/TAP.2021.3102135.
• C-J-1 Guo R, Lin Z, Shan Tao, et al., Solving Combined Field Integral Equation With Deep Neural Network for 2-D Conducting Object, IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 4, pp. 538-542, April 2021, doi: 10.1109/LAWP.2021.3056460.

🏷️ Conference Paper

First/Corresponding-author Publications

• I-C-6 Shan Tao, Solving Electromagnetic Scattering of 3D PEC Targets Based on Graph Neural Networks, 2024 International Applied Computational Electromagnetics Society Symposium (ACES-China), Xi’an, China, 2024, pp. 1-2, doi: 10.1109/ACES-China62474.2024.10699762.
• I-C-5 Shan Tao, Li M, Yang F and Xu S, Hardware-friendly Unsupervised Coding Scheme for Reconfigurable Intelligent Surface Based on Binary Neural Networks, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022, pp. 449-450, doi: 10.1109/APS/USNC-URSI47032.2022.9887254.
• I-C-4 Shan Tao, Lin Z, Song X, et al. A New Approach for Solving Inverse Scattering Problems Based on Physics informed Supervised Residual Learning, 2022 16th European Conference on Antennas and Propagation (EuCAP). IEEE, 2022: 1-4.
• I-C-3 Shan Tao, Song X, Guo R, et al. Physics-informed Supervised Residual Learning for Electromagnetic Modeling, 2021 International Applied Computational Electromagnetics Society Symposium (ACES). IEEE, 2021: 1-4.
• I-C-2 Shan Tao, Li M, Xu S, et al. Synthesis of reflectarray based on deep learning technique, 2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC). IEEE, 2018: 1-2.
• I-C-1 Shan Tao, Dang X, Li M, et al. Study on a 3D Possion’s equation slover based on deep learning technique, 2018 IEEE International Conference on Computational Electromagnetics (ICCEM). IEEE, 2018: 1-3.

Co-Authored Publications

• C-C-3 Zeng J, Shan Tao, Li M, et al. Neural Contrast Source Inversion Method Based on Single-frequency Data, 2022 International Applied Computational Electromagnetics Society (ACES-China) Symposium (accepted)
• C-C-2 Shao T, Shan Tao, Li M, et al. A Poisson’s Equation Solver Based on Neural Network Precondtioned CG Method, 2022 International Applied Computational Electromagnetics Society (ACES-China) Symposium (accepted)
• C-C-1 Tang W, Shan Tao, Dang X, et al. Study on a Poisson’s equation solver based on deep learning technique, 2017 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS). IEEE, 2017: 1-3.

💬 Talks

• 2025.10.10-10.12, ‘A Novel Approach for 2D Electromagnetic Imaging of Human Brain Based on ODIL’, The 8th International Symposium on Electromagnetic Compatibility (ISEMC 2025), Hefei, China
• 2025.10.10-10.12, ‘A New Approach for 2D Microwave Imaging Based on ODIL’, The 8th International Symposium on Electromagnetic Compatibility (ISEMC 2025), Hefei, China
• 2025.08.08-08.11, ‘A New Approach for 2-D EIT Inversion Based on Optimizing a Discrete Loss Framework’, 2025 International Applied Computational Electromagnetics Society Symposium (ACES-China 2025), Huangshan, China
• 2025.08.08-08.11, ‘A Data-driven approach for Solving 2D Combined-field Integral Equations Based on WaveKAN’, 2025 International Applied Computational Electromagnetics Society Symposium (ACES-China 2025), Huangshan, China
• 2025.07.29-08.01, ‘Study on Deep Learning Assisted Microwave Breast Imaging’, 2025 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), Tianjin, China
• 2024.08.16-08.19, ‘Solving Electromagnetic Scattering of 3D PEC Targets Based on Graph Neural Networks’, 2024 International Applied Computational Electromagnetics Society Symposium (ACES-China 2024), Xi’an, China

📎 Academic Service

Reviewer

• IEEE Transactions on Antennas and Propagation
• IEEE Transactions on Geoscience and Remote Sensing
• IEEE Open Journal of Antennas and Propagation
• IEEE Transactions on Microwave Theory and Techniques

Chaired conference sessions

• AI enhanced computational algorithms for electromagnetic modeling and inversion in 2025 International Applied Computational Electromagnetics Society (ACES-China) Symposium, HuangShan, China, Augst 08 - Augst 11, 2025.
• Machine Learning-assisted Electromagnetic Modeling and Imaging in 2022 International Applied Computational Electromagnetics Society (ACES-China) Symposium, Xuzhou, China, July28 - July 31, 2022.

📚 Teaching

• 2025 Fall: EDA for RF Integrated Circuits, for graduate students.
• 2025 Spring: Electromagnetic fields and waves, for undergraduate students.
• 2024 Spring: Electromagnetic fields and waves, for undergraduate students.
• 2024 Fall: Equations of Mathematical Physics and Special Functions, for undergraduate students.

🎖 Honors and Awards

• 2024.04 URSI GASS 2023 Young Scientist Award
• 2023.08 PIERS 2023 Young Scientist Award
• 2022.12 ACES-China 2022 Best Student Paper Award (3rd Prize)
• 2021.08 ACES 2021 Best Student Paper Award (1st Prize)
• 2021.03 Marina Forum on EMeatmaterials Best Student Paper Award (2nd Prize)
• 2018.03 ICCEM Best Student Paper Honorable Mention Award

📖 Educations

• 2016.09 - 2021.06, Ph.D. in Electronics Engineering, Tsinghua University, Beijing, China.
• 2012.09 - 2016.06, B.S. in Electronics Engineering, Xidian University, Xi’an, China.

💻 Work History

• 2023.09 - present, Associate Professor, Beihang University, Beijing, China.
• 2021.07 - 2023.08, Post-Doctoral Researcher, Tsinghua University, Beijing, China.
• 2016.09 - 2021.06, Research assistant, Tsinghua University, Beijing, China.
• 2020.07 - 2020.12, Intern student, Apple Research Development Center, Beijing, China.
• 2019.07 - 2019.12, Intern student, Schlumberger Doll Research Center, MA, USA.
• 2018.06 - 2018.09, Intern student, Schlumberger Beijing Geoscience Center, Beijing, China.