• 自然语言处理

• 机器学习

• 知识表示

• 计算机视觉

• 人工智能

屠可伟，信息学院副教授，博士生导师，目前在学院担任视觉与数据智能中心主任、毕业工作委员会主任。

屠可伟博士，上海科技大学信息科学与技术学院副教授、博士生导师。于2002和2005年在上海交通大学计算机科学与工程系获学士和硕士学位；2012年于美国爱荷华州立大学获计算机科学博士学位；2012至2014年在美国加州大学洛杉矶分校统计系与计算机系从事博士后研究工作。研究方向包括自然语言处理、机器学习、知识表示等人工智能领域，目前侧重于研究语言结构的表示、学习与应用。
更多信息请参见他的个人主页： http://faculty.sist.shanghaitech.edu.cn/faculty/tukw/

1. Wenjuan Han, Ge Wang, Yong Jiang and Kewei Tu, “Multilingual Grammar Induction with Continuous Language Identification”, in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, China, November 3–7, 2019.

2. Yong Jiang, Wenjuan Han and Kewei Tu, “A Regularization-Based Framework for Bilingual Grammar Induction”, in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, China, November 3–7, 2019.

3. Liwen Zhang, Kewei Tu and Yue Zhang, “Latent Variable Sentiment Grammar”, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, July 28 – August 2, 2019.

4. Wenjuan Han, Yong Jiang and Kewei Tu, “Enhancing Unsupervised Generative Dependency Parser with Contextual Information”, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, July 28 – August 2, 2019.

5. Yunzhe Yuan, Yong Jiang and Kewei Tu, “Bidirectional Transition-Based Dependency Parsing”, in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, USA, January 27 - February 1, 2019.

6. Yanpeng Zhao, Liwen Zhang and Kewei Tu, “Gaussian Mixture Latent Vector Grammars”, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, July 15–20, 2018. 

7. Jun Mei, Yong Jiang and Kewei Tu, “Maximum A Posteriori Inference in Sum-Product Networks”, in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, Lousiana, USA, February 2–7, 2018.

8. Yong Jiang, Yang Zhou and Kewei Tu, “Learningand Evaluation of Latent Dependency Forest Models”, to appear in Neural Computingand Applications.

9. Yong Jiang, Wenjuan Han and Kewei Tu, “Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 7–11, 2017.

10. Wenjuan Han, Yong Jiang and Kewei Tu, “Dependency Grammar Induction with Neural Lexicalization and Big Training Data”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 7–11, 2017.

11. Jiong Cai, Yong Jiang and Kewei Tu, “CRF Autoencoder for Unsupervised Dependency Parsing”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 7–11, 2017.

12. Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu and Dan Goldwasser, ‘Semi-supervised Structured Prediction with Neural CRF Autoencoder”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 7–11, 2017.

13. Shanbo Chu, Yong Jiang and Kewei Tu, “Latent Dependency Forest Models”, in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017), San Francisco, California, USA, February 4–9, 2017.

14. Lin Qiu, Kewei Tu and Yong Yu, “Context-Dependent Sense Embedding”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016.

15. Yong Jiang, Wenjuan Han and Kewei Tu, “Unsupervised Neural Dependency Parsing”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016.

16. Kewei Tu, “Modified Dirichlet Distribution: Allowing Negative Parameters to Induce Stronger Sparsity”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016. 

17. Kewei Tu, “Stochastic And-Or Grammars: A Unified Framework and Logic Perspective”, in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York City, USA, July 9-15, 2016.

18. Kewei Tu, Meng Meng, Mun Wai Lee, Tae Eun Choe, and Song-Chun Zhu, “Joint Video and Text Parsing for Understanding Events and Answering Queries”, in Proceedings of IEEE MultiMedia, vol. 21, no. 2, pp. 42-70, 2014.