Ming Li

 Brief Biography

Currently I am a Professor at LAMDA led by Professor Zhi-Hua Zhou, my Ph. D. supervisor. 

I received my B. Sc. and Ph. D. degrees in computer science from Department of Computer Science and Technology, Nanjing University, China, in 2003 and 2008, respectively. I joined Department of Computer Science and Technology of Nanjing University in 2008.

My major research interests include machine learning and data mining, especially on software mining. major research interests include machine learning and data mining, especially on software mining. I have served as the area chair of IEEE ICDM, senior PC member of the premium conferences in artificial intelligence such as IJCAI and AAAI, and PC members for other premium conferences such as KDD, NIPS, ICML, etc., and the chair of the International Workshop on Software Mining. I have served as the associate editor (junior) for Frontiers of Computer Science and editorial board member for International Journal of Data Warehousing and Mining. I am the executive board member of ACM SIGKDD China Chapter. I have been granted various awards including the Excellent Youth Award from NSFC, the New Century Excellent Talents program of the Education Ministry of China, the CCF Distinguished Doctoral Dissertation Award, and Microsoft Fellowship Award, etc.

Research Interest

My current research interests mainly include Machine Learning, Data Mining, especially on Software Mining.

Selected Publications     [Full List]

• X. Huo, F. Thung, M. Li, D. Lo, and S.-T. Shi. Deep transfer bug localization. IEEE Transactions on Software Engineering, in press.

• X. Huo, M. Li, and Z.-H. Zhou. Control flow graph embedding based on multi-instance decomposition for bug localization. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

• S.-T. Shi, W. Zheng, J. Tang, Q.-G. Chen, Y. Hu, J. Zhu, and M. Li. Deep time-stream framework for click-through rate prediction by tracking interest evolution. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

• Y.-Y. Zhang and M. Li. Find me if you can: Deep software clone detection by exploiting the contest between the plagiarist and the detector. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

• S.-T. Shi, M. Li,, D. Lo, F. Thung, and X. Huo. Automatic code review by learning the revision of source code. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

• Z.-Y. Shen and M. Li. T2S: Domain adaptation via model-independent inverse mapping and model reuse. In: Proceedings of the 18th International Conference on Data Mining (ICDM'18), Singapore, 2018, pp.1224-1229.

• Z. Xie and M. Li. Cutting the software building efforts in continuous integration by semi-supervised online AUC optimization. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2875-2881.

• H.-H. Wei and M. Li. Positive and unlabeled learning for detecting software functional clones with adversarial training. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2840-2846.

• Z. Xie and M. Li. Semi-supervised AUC optimization without guessing labels of unlabeled data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, pp.4310-4317.

• A.-S. Ni and M. Li. ACONA: active online model adaptation for predicting continuous integration build failures. In: Proceedings of the 40th International Conference on Software Engineering: Companion Volume (ICSE'18), Gothenburg, Sweden, 2018, pp.366-367.

• X. Huo and M. Li. Enhancing the unified features to locate buggy files by exploiting the sequential nature of source code. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, 1909-1915.

• H.-H. Wei and M. Li. Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, 3034-3040.

• A.-S. Ni and M. Li. Cost-effective build outcome prediction using cascaded classifiers. In: Proceedings of the 14th International Conference on Mining Software Repositories (MSR'17), Buenous Aires, Argentina, 2017.

• X. Huo, M. Li, and Z.-H. Zhou. Learning unified features from natural and programming languages for locating buggy source code. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, 1606-1612. 

• T.-D. Le, D. Lo, and M. Li. Constrained feature selection for localizing faults. In: Proceedings of the 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME'15), Bremen, Germany, 2015, pp. 501-505. 

• M. Li, H. Zhang, R. Wu, and Z.-H. Zhou. Sample-based software defect prediction with active and semi-supervised learning. Automated Software Engineering, 2012, 19(2): 201-230.

• Y. Jiang, M. Li, and Z.-H. Zhou. Software defect detection with ROCUS. Journal of Computer Science and Technology, 2011, 26(2): 328-342.

• Introduction to Data Mining             Spring 2020, 2019, 2018, 2017, 2016, 2015

• Advanced Data Mining                    Spring 2019

• Artificial Intelligence                      Spring 2014, 2013, 2012, 2011

• Data Mining (081202B3)               Fall 2011, 2010, 2009

• Digital Image Processing                Spring 2009

Correspondence