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Apr 6, 2021 16:37

职称 Associate Professor 部门 Department of Computer Science and Technology
加盟部门 2007 邮箱 yingz@tsinghua.edu.cn
电话 +86-10-62783505-8006


• Ying ZHAO

• Associate Professor

• Department of Computer Science and Technology

• Joined Department: 2007

• Email: yingz@tsinghua.edu.cn

• Phone: +86-10-62783505-8006

Education background

Bachelor of Computer Science, Peking University, Beijing, China, 1999;

Ph.D. in Computer Science, University of Minnesota, USA, 2005.

Areas of Research Interests

Data Mining, Machine Learning

Research Status

My research group focuses on fundamental problems in data mining and machine learning and their applications on scientific data and urban spatial-temporal data. Recently, we addressed the problem of uncertainty qualification for deep neural networks and proposed a novel way of estimating model uncertainty by using both training and test-time augmentation. We also proposed novel indexing methods for big spatial-temporal data, based on which K-nn and reachability queries can be answered effectively to support various real-world applications, such as ride sharing and containing disease spreading.

Honors And Awards

The course “Combinatorics and Algorithm Design” named as National Quality Curriculum Taught in English for International Students (awarded by Ministry of Education, 2017).

IBM Research Award (awarded by China Scholarship Council, 2007).

Academic Achievement

AI for Science

[1] Ziheng Zhou, Ying Zhao, Yiyu Qing, Wenming Jiang, Yihan Wu, Wenguang Chen. A Physics-guided NN-based Approach for Tropical Cyclone Intensity Estimation. Proceedings of the 2023 SIAM International Conference on Data Mining (SDM23), pp. 271–279, 2023.

[2] Wenming Jiang, Ying Zhao, Yihan Wu, Haojia Zuo. Capturing Model Uncertainty with Data Augmentation in Deep Learning. Proceedings of the 2022 SIAM International Conference on Data Mining (SDM22), pp. 271–279, 2022.

[3] Wenming Jiang, Ying Zhao, Zehan Wang. Risk-Controlled Selective Prediction for Regression Deep Neural Network Models. Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020.

Spatial-Temporal Data Mining

[4] Lohan Meunier, Ying Zhao. Reachability Queries on Dynamic Temporal Bipartite Graphs. Proceedings of the 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023), accepted, Hamburg, Germany, 2023.

[5] Haojia Zuo, Bo Cao, Ying Zhao, Bilong Shen, Weimin Zheng, Yan Huang. High-capacity ride-sharing via shortest path clustering on large road networks. Journal of Supercomputing, 77(4), 4081-4106, 2021.

[6] Bilong Shen, Ying Zhao, Guoliang Li, Weimin Zheng, Yue Qin, Bo Yuan, Yongming Rao. V-Tree: Efficient kNN Search on Moving Objects with Road-Network Constraints. Proceedings of the 33th IEEE International Conference on Data Engineering (ICDE17), pp. 609-620, San Diego, USA, 2017.


[7] Ying Zhao and George Karypis. Hierarchical Clustering Algorithms for Document Datasets. Data Mining and Knowledge Discovery, vol.10, no. 2, pp. 141-168, 2005.

[8] Ying Zhao and George Karypis. Topic-driven Clustering for Document Datasets. Proceedings of the 2005 SIAM International Conference on Data Mining (SDM05), pp. 358-369, 2005.

[9] Ying Zhao and George Karypis. Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering. Machine Learning, vol. 55, no. 3, pp. 311-331, 2004.

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