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Mei Yu

 Mei Yu



Tianjin International Engineering Institute




College of Intelligence and Computing




B209 Room, Building 55



Professor Yu is mainly engaged in data mining, artificial intelligence, educational technology. She and her team have published many related academic papers, and have obtained a number of authorized patents and software copyrights.

As the coach of the ACM-ICPC and CCPC teams of Tianjin University, she is responsible for the selection of members, the centralized training of the team, and leading the team to participate in international and domestic competitions. The ACM-ICPC team of Tianjin University has made outstanding achievements in the Asian Regional Championships.


Research Interests

Research on natural language processing and knowledge mapping

Research and application of adaptive learning resource recommendation system

Research on wind power prediction

Research on user modeling, emotion analysis, recommendation algorithm, network resource search and algorithm optimization in social networks

Study on medical ultrasonic image aided diagnosis



(1) Ruixuan Zhang, Wenhuan Lu, Xi Wei, Jialin Zhu, Han Jiang, Zhiqiang Liu, Jie Gao, Xuewei Li*, Jian Yu, Mei Yu, Ruiguo Yu. A Progressive Generative Adversarial Method for Structurally Inadequate Medical Image Data Augmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 26(1): 7-16.

(2) Xiang Ying, Zechen Meng, Mankun Zhao, Mei Yu, Shirui Pan, Xuewei Li*. Gated graph convolutional network with enhanced representation and joint attention for distant supervised heterogeneous relation extraction[J]. World Wide Web, 2021: 1-20.

(3) Tao Luo, Tong Xu, Jian Yu, Xuewei Li*, Xi Wei, Mei Yu, Ruixuan Zhang, Jie Gao, Ruiguo Yu. Border Sensitive Network in Weakly Supervised Thyroid Nodule Detection for Ultrasound Image[C]. International Conference on Bioinformatics & Biomedicine, 2021, 1429-1432.

(4) Mei Yu, Ming Han, Xuewei Li*, Xi Wei, Jialin Zhu , Han Jiang ,Zhiqiang Liu, Ruixaun Zhang, Ruiguo Yu. SSE: Scale-adaptive Soft Erase Weakly Supervised Segmentation Network for Thyroid Ultrasound Images[C]. International Conference on Bioinformatics & Biomedicine, 2021, 1615-1618.

(5) Mei Yu, Junbin Wei, Chenhan Wang, Han Jiang, Jian Yu, Ruixuan Zhang, Xuewei Li*, Ruiguo Yu. Edge Enhancement Network for Weakly Supervised Semantic Segmentation[C]. International Conference on Multimedia & Expo, 2021, 1-6.

(6) Xiang Ying, Yulin Zhang, Xi Wei, Mei Yu, Jialin Zhu, Jie Gao, Zhiqiang Liu, Xuewei Li*, Ruiguo Yu*. MSDAN: Multi-Scale Self-Attention Unsupervised Domain Adaptation Network for Thyroid Ultrasound Images[C]. International Conference on Bioinformatics & Biomedicine, 2020, 871-876.

(7) Ruiguo Yu, Jiachen Hu, Mei Yu, Xi Wei, Han Jiang, Jialin Zhu, Zhiqiang Liu, Jie Gao, Xuewei Li*. Boundary-aware Segmentation Network Using Multi-Task Enhancement for Ultrasound Image[C]. International Conference on Bioinformatics & Biomedicine, 2020, 1210-1214.

(8) Mei Yu, Minyutong Cheng, Xubin Li, Zhiqiang Liu, Jie Gao, Xuzhou Fu, Xuewei Li*, Ruiguo Yu. Tumor Classification Based on Approximate Symmetry Using Dual-Branch Complementary Fusion Network[C]. International Conference on Bioinformatics & Biomedicine, 2020, 1215-1218.

(9) Mei Yu, Zhuo Zhang, Xuewei Li*, Jian Yu, Jie Gao, Zhiqiang Liu, Bo You, Xiaoshan Zheng, Ruiguo Yu*. Superposition Graph Neural Network for offshore wind power prediction[J]. Future Generation Computer Systems, 2020, 113, 145-157.

(10) Tao Luo, Yifan Wei, Mei Yu, Xuewei Li, Mankun Zhao, Tianyi Xu, Jian Yu, Jie Gao and Ruiguo Yu*. BTDE: Block Term Decomposition Embedding for Link Prediction in Knowledge Graph[C]. European Conference on Artificial Intelligence, 2020, 817-824.

(11) Ruiguo Yu, Zhiqiang Liu, Xuewei Li, Wenhuan Lu, Degang Ma, Mei Yu, Jianrong Wang, Bin Li. Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space[J]. Applied Energy, 2019(238): 249-257.

(12) Ruiguo Yu, Jie Gao, Mei Yu, Wenhuan Lu, Tianyi Xu, Mankun Zhao, Jie Zhang, Ruixuan Zhang, Zhuo Zhang. LSTM-EFG for wind power forecasting based on sequential correlation features[J]. Future Generation Computer Systems, 2018(93): 33-42.


Teaching Experience

Data Mining

Practice of Data Mining

Advanced Data Structure

Advanced Algorithm