Xin Mu （慕鑫）

Xin Mu
Ph.D., Assistant Researcher Fellow
Peng Cheng Laboratory

Department of Mathematics and Theories
Peng Cheng Laboratory
Email: muxin505gmail.com or muxlamda.nju.edu.cn or muxpcl.ac.cn

About Me

From Mar. 2020, I am an assistant research fellow at the Department of Mathematics and Theories, Peng Cheng Laboratory(In Shen Zhen, China). My recent research interests lie in the area of data mining in the digital economy, including some sub-fields of data auditing, data incenttive and data trading, etc. I am looking for highly-motivated students to work together, please send me your CV if interested.

From Sep. 2018 to Mar. 2020, I worked as a senior researcher at Advertising and Marketing Services, Tencent Inc, and a member of user profile modeling group. I mainly focused on creating deep learning models for user profile modeling and also working on building NLP capacity in advertising recommendation system.

In Sep. 2018, I got my Ph.D. in LAMDA Group at Nanjing University, advised by Prof. Zhi-Hua Zhou, Before that, I received my B.Sc. degree and M.Sc. degree from Jiangnan University, Wuxi China in 2010 and 2013 respectively.

From May. 2015 to Mar. 2018, I was visiting at Singapore Management University in LARC lab as research assistant, supervised by Prof. Feida Zhu and Prof. Ee-Peng Lim.

Research Interests

My research interests include some sub-fields of Data Mining and Machine Learning:

User Profile Modeling (UPM) is to creat a user profile prediction model in advertising recommendation system, which can provide more precise information about CTR of each user.

Streaming Classification under Emerging New Class (SENC) is that in the streaming classification problem, new classes may emerge as the environment changes. The predictive accuracy of a previously trained classifier will be severely degraded if it is used to classify instances of a previously unseen class in a data stream.

User Idenetity Linkage (UIL) aims to identify the accounts of the same user across different social platforms.

Publications

Journal Articles

X. Mu, and K. M. Ting, Z.-H. Zhou. Classification under streaming emerging new classes: A solution using completely random trees, IEEE Transactions on Knowledge and Data Engineering(TKDE), 2017, 29(8): 1605-1618. [code] Please feel free to contact mux@lamda.nju.edu.cn for this code.

X.-S. Wei, H.-J. Ye, X. Mu, J. Wu, C. Shen and Z.-H. Zhou. Multiple Instance Learning with Emerging Novel Class. IEEE Transactions on Knowledge and Data Engineering(TKDE), 2020.

X.-S. Wei, X. Mu, and Y. Yang. An application in medical data mining based on twice ensemble learning (in chinese with english abstract). Journal of Frontiers of Computer Science and Technology, 2014, 8(9): 1113-1119.

Conference Papers

J. Pei, F. Zhu, Z. Cong, X. Luo, H. Liu, X. Mu. Data Pricing and Data Asset Governance in the AI Era. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), 2021.

X.-Q. Cai, P. Zhao, K. M. Ting, X. Mu, Y. Jiang. Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes. In: Proceedings of the 19th IEEE International Conference on Data Mining (ICDM'19), Beijing, China, 2019.

L. Liu, F. Mu, P. Li, X. Mu, J. Tang, X. Ai, R. Fu, L. Wang and X. Zhou. NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL'19), Italy, 2019.

X. Mu, W. Xie, Roy, F. Zhu and E.-P. Lim. AD-Link: An Adaptive Approach for User Identity Linkage. In: Proceedings of the 9th IEEE International Conference on Big Knowledge (ICBK'19), Beijing, China, 2019.

W. Xie, X. Mu, Roy. Lee, F. Zhu, E.-P. Lim. Unsupervised User Identity Linkage via Factoid Embedding. In: Proceedings of the 18th IEEE International Conference on Data Mining (ICDM'18)), Singapore, 2018.

S.-J. Huang, J.-L. Chen, X. Mu, and Z.-H. Zhou. Cost-effective active learning from diverse labelers. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17)), Melbourne, Australia, 2017.

X. Mu, F. Zhu, J. Du, E.-P. Lim, and Z.-H. Zhou. Streaming classification with emerging new class by class matrix sketching. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

S.-J. Huang, J.-L. Chen, X. Mu, and Z.-H. Zhou. Cost-effective active learning from diverse labelers. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17)), Melbourne, Australia, 2017.

X. Mu, F. Zhu, E.-P. Lim, J. Xiao, J. Wang, and Z.-H. Zhou. User identity linkage by latent user space modelling. In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'16), San Francisco, CA, 2016, pp.1775-1784.

Contests

The first runner-up in the Data Mining Competition (in association with CCDM 2014), 2014.