肖理业，电子科技大学物理学院副教授、博士生导师。

2015年6月于电子科技大学获得电子信息科学与技术专业学士学位，2019年6月获得无线电物理专业博士学位。

2019年入选“博士后创新人才支持计划”，于厦门大学电子科学与技术学院从事博士后研究。

2021年7月入选厦门大学“南强青年拔尖人才B类”聘为副教授、博士生导师。

2025年4月加入电子科技大学物理学院，任副教授、博士生导师。

主要从事高对比度、电大尺寸电磁逆散射问题；大规模建模变量的电磁器件优化问题;基于机器学习的电磁器件设计建模高效性、设计自由度及应用普适性问题。相关研究成果已发表高水平SCI论文47篇，其中第一或通讯作者论文45篇（中科院二区及以上SCI论文42篇），ESI高被引论文3篇；主持国家自然科学基金青年基金、国家博士后创新人才支持计划项目、5项校企横向项目，参与1项国家重点研发计划课题、1项973课题；授权5项国家发明专利。

博士后创新人才支持计划优秀创新成果、中国电子教育学会“优秀博士论文提名奖”、电子科技大学“成电杰出学生”；2023年、2024年连续两年入选斯坦福大学“全球前2%顶尖科学家榜单”。

l  学术专著Chapter

[1] B.-Z. Wang, Li-Ye Xiao, W. Shao, Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning, Chapter: Advanced Neural Networks for Electromagnetic Modeling and Design, IEEE-Wiley.

[2] Q. H. Liu, Li-Ye Xiao, R. Hong, H.-J. Hu, Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning, Chapter: Deep Learning for High Contrast Inverse Scattering of Electrically Large Structures, IEEE-Wiley.

l  学术论文(*通讯作者)

[1]    Zhao-Qing Xu, Li-Ye Xiao^*, Yan-Fang Liu, Wei Shao, “Machine learning-based inverse design of angle-stable frequency selective surfaces with hexagonal unit cells,” IEEE Antennas and Wireless Propagation Letters, vol. 25(8), pp. 2267 - 2271, Aug. 2025.

[2]    Zhao-Qing Xu, Li-Ye Xiao^*, Yi-Fan Xie, Sheng Sun, Wei Shao, Qing Huo Liu, “Machine learning-based-inverse topological design method for active frequency-selective surface (ITDM-AFSS)” IEEE Transactions on Antennas and Propagation, vol. 73, no. 8, pp. 6079 - 6084, Aug. 2025.

[3]    Xin-Yue Qi, Li-Ye Xiao, Hao Lv, Yan-Fang Liu, Wei Shao, “A machine learning-based inverse design method for frequency-selective surface microwave absorbers,” Engineering Applications of Artificial Intelligence, vol.160, pp. 111842, 2025.

[4]    Yan-Fang Liu, Li-Ye Xiao^*, Qing Huo Liu, “Machine learning-based design scheme for multifunctional antenna arrays with reconfigurable scattering patterns”, IEEE Transactions on Antennas and Propagation, vol. 73, no. 7, pp. 4535 - 4548, Jul. 2025.

[5]    Xin-Yue Qi, Li-Ye Xiao, Hao Lv, Yan-Fang Liu, Wei Shao, “A machine learning-based inverse design method for frequency-selective surface microwave absorbers,” Engineering Applications of Artificial Intelligence, vol.160, pp. 111842, 2025.

[6]    Yan-Fang Liu, Li-Ye Xiao^*, Wei Shao, Lin Peng, Qing Huo Liu, “An efficient training data collection method for machine learning-based frequency selective surface design,” IEEE Antennas and Wireless Propagation Letters, vol. 23(12), pp. 4568 - 4572, Dec. 2024.

[7]    Yan-Fang Liu, Li-Ye Xiao^*, Wei Shao, Lin Peng, Qing Huo Liu, “A machine learning-enabled radiation-scattering integrated design approach for low-scattering phased arrays,” IEEE Antennas and Wireless Propagation Letters, vol. 23(12), pp. 4169 - 4173, Dec. 2024.

[8]    Hao Lv, Li-Ye Xiao*, Hao-Jie Hu, Qing Huo Liu, “A Spatial Inverse Design Method (SIDM) Based on Machine Learning for Frequency Selective Surface (FSS) Structures,” IEEE Transactions on Antennas and Propagation, vol. 72, no. 3, pp. 2434-2444, Mar. 2024.

[9]    Xuanying Hou, Hao-Jie Hu, Li-Ye Xiao*, Ke Chen, Mingwei Zhuang, Qing Huo Liu*, “A Machine-Learning-Based Method to Accelerate the Design of SAW Filters at Different Frequency Bands”, IEEE Sensors Journal, vol. 24, issue: 16, Aug. 2024.

[10] Li-Ye Xiao, Yu Cheng, Yan-Fang Liu, Fu-Long Jin, Qing Huo Liu*, “An Inverse Topological Design Method (ITDM) Based on Machine Learning for Frequency-Selective-Surface (FSS) Structures,” IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 653-663, Jan. 2024.

[11] Hao-Jie Hu, Jiawen Li, Li-Ye Xiao*, Yu Cheng, Qing Huo Liu*, “A residual fully convolutional network (Res-FCN) for electromagnetic inversion of high contrast scatterers at an arbitrary frequency within a wide frequency band”, Inverse Problems, vol.40, no.7, May 2024.

[12] Lianmu Chen, Li-Ye Xiao*, Jiawen Li, Hao-Jie Hu, Mingwei Zhuang, Qing Huo Liu*, “A field data transformation-joint inversion scheme (FDT-JIS) for petrophysical inversion with electromagnetic and acoustic data”, IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 5914811, May 2024.

[13] Ruichen Luo, Lixiao Wang, Zheng He, Fen Xiao, Li-Ye Xiao*,Qing Huo Liu*, “A Wideband and Flexible Testing System Based on a Quasi-Coaxial Structure and Machine Learning,” IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 3, pp. 1766-1774, Mar. 2024.

[14] Hao-Jie Hu, Li-Ye Xiao*, Jiawen Li, Qing Huo Liu*, “A Hybrid Forward-Inverse Neural Network With the Transceiver-Configuration-Independent Technique for the Wideband Electromagnetic Inverse Scattering Problem,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-11, 2024.

[15] Lianmu Chen, Li-Ye Xiao*, Hao-Jie Hu, Mingwei Zhuang, Qing Huo Liu*, “Multimodule Deep Learning Scheme for Elastic Wave Inversion of Inhomogeneous Objects with High Contrasts,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-10, 2023.

[16] Li-Ye Xiao, J. Yi, Y. Mao, X. Qi, R. Hong*, and Q. Huo Liu*. A Novel Optical Proximity Correction (OPC) System Based on Deep Learning Method for the Extreme Ultraviolet (EUV) Lithography. Progress in Electromagnetics Research, 176: 95-108, 2023. (Invited)

[17] Jiawen Li, Hao-Jie Hu, Jianwen Wang, Mingwei Zhuang, Li-Ye Xiao*, Qing Huo Liu*, “A Transceiver-Configuration-Independent 2-D Electromagnetic Full-Wave Inversion Scheme Based on End-to-End Artificial Neural Networks,” IEEE Transactions on Antennas and Propagation, vol. 71, no. 5, pp. 4600-4605, May 2023.

[18] Jiawen Li, Zhen Guan, Jianwen Wang, Li-Ye Xiao*, Qing Huo Liu*, “Contracting Electromagnetic Full-Wave Inversion of 2-D Inhomogeneous Objects with Irregular Shapes Based on the Hybrid SESI Forward Solver”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 5305411, Dec. 2022.

[19] Li-Ye Xiao, Ronghan Hong*, Le-Yi Zhao, Hao-Jie Hu, Qing Huo Liu*, “A Hybrid Neural Network Electromagnetic Inversion Scheme (HNNEMIS) for Super-Resolution 3-D Microwave Human Brain Imaging”, IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6277-6286, Aug. 2022.

[20] Le-Yi Zhao, Li-Ye Xiao*, Yu Cheng, Ronghan Hong, Qing Huo Liu, “Machine-Learning-Based Inversion Scheme for Super-Resolution Three-Dimensional Microwave Human Brain Imaging,” IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 12, pp. 2437-2441, Dec. 2022.

[21] Yu Cheng, Li-Ye Xiao*, Le-Yi Zhao, Ronghan Hong and Qing Huo Liu*. A 3-D Full Convolution Electromagnetic Reconstruction Neural Network (3-D FCERNN) for Fast Super-Resolution Electromagnetic Inversion of Human Brain. Diagnostics, 12(11): 2786-2801, 2022.

[22] Li-Ye Xiao, Jiawen Li, Feng Han, Mingwei Zhuang*, Qing Huo Liu*, “Super-Resolution 3-D Microwave Imaging of Objects with High Contrasts by a Semijoin Extreme Learning Machine”, IEEE Transactions on Microwave Theory and Techniques, vol. 69, no. 11, pp. 4840 - 4855, Aug. 2021.

[23] Hao-Jie Hu, Li-Ye Xiao*, Jun-Nan Yi, Qing Huo Liu*, “Nonlinear Electromagnetic Inversion of Damaged Experimental Data by a Receiver Approximation Machine Learning Method”, IEEE Antennas and Wireless Propagation Letters, vol. 20, no.7, July 2021.

[24] Li-Ye Xiao, Wei Shao*, Fu-Long Jin, Bing-Zhong Wang, Qing Huo Liu, “Inverse Artificial Neural Network for Multiobjective Antenna Design”, IEEE Transactions on Antennas and Propagation, vol. 69, no. 10, pp. 6651-6659, 2021.

[25] Li-Ye Xiao, Jiawen Li, Feng Han, Wei Shao, Qing Huo Liu*, “Dual-Module NMM-IEM Machine Learning for Fast Electromagnetic Inversion of Inhomogeneous Scatterers with High Contrasts and Large Electrical Dimensions”, IEEE Transactions on Antennas and Propagation, vol. 68, no. 8, pp. 6245-6255, Aug. 2020.

[26] Li-Ye Xiao, Fu-Long Jin, Bing-Zhong Wang, Qing Huo Liu, Wei Shao*, “Efficient Inverse Extreme Learning Machine for Parametric Design of Metasurfaces”, IEEE Antennas and Wireless Propagation Letters, vol. 68, no. 4, pp. 1260-1269, Apr. 2020.

[27] Li-Ye Xiao, Wei Shao*, Fu-Long Jin, Bing-Zhong Wang, W. Joines, Qing Huo Liu, “Semisupervised Radial Basis Function Neural Network with an Effective Sampling Strategy”, IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 4, pp. 1260-1269, Apr. 2020.

[28] Li-Ye Xiao, Wei Shao*, Fu-Long Jin, Bing-Zhong Wang, Qing Huo Liu, “Radial Basis Function Neural Network With Hidden Node Interconnection Scheme for Thinned Array Modeling”, IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 12, pp. 2418-2422, 2020.

[29] Li-Ye Xiao, Wei Shao*, Xiao Ding, Qing Huo Liu, William T. Joines, “Multigrade Artificial Neural Network for the Design of Finite Periodic Arrays”, IEEE Transactions on Antennas and Propagation, vol. 67, no. 5, pp. 3109-3116, 2019.

[30] Li-Ye Xiao, Wei Shao*, Xiao Ding, Bing Zhong Wang, “Dynamic Adjustment Kernel Extreme Learning Machine for Microwave Component Design”, IEEE Transactions on Microwave Theory and Techniques, vol. 66, no. 10, pp. 4452-4461, Oct. 2018.

[31] Li-Ye Xiao, W. Shao*, Z. X. Yao, S. S. Gao, “Data Mining Techniques in Artificial Neural Network for UWB Antenna Design”, Radioengineering, vol. 27, no. 1, pp. 70-78, Apr. 2018.

[32] Li-Ye Xiao, W. Shao*, S. B. Shi, B. Z. Wang, “Extreme Learning Machine with A Modified Flower Pollination Algorithm For Filter Design”, Applied Computational Electromagnetics Society Journal, vol. 33, No. 3, Mar. 2018.

[33] Li-Ye Xiao, Wei Shao*, Fu-Long Jin, Bing-Zhong Wang, “Multiparameter Modeling with ANN for Antenna Design”, IEEE Transactions on Antennas and Propagation, vol. 66, no. 7, pp. 3718 - 3723, 2018.