Man must explore


Why not change the world!

Research Interests


  • Machine Learning/ Deep Learning
    • Representation Learning
    • Graph Neural Networks
    • Memory Networks
  • Natural Language Processing
    • Question Answering
    • Text Generation
    • Text Representation
    • Topic Modelling

Publications


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Preprints

  1. Yu Chen, Lingfei Wu and Mohammed J. Zaki, Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks.

Conference Publications

  1. [ICLR 2021] Shangqing Liu, Yu Chen**, Xiaofei Xie**, Jing Kai Siow and Yang Liu (**Corresponding Author), Retrieval-Augmented Generation for Code Summarization via Hybrid GNN. In Proceedings of the 9th International Conference on Learning Representations, Virtual Event, May 4-8, 2021. Spotlight paper. Acceptance rate=3.8% (114/2997).
  2. [WSDM 2021] Yu Chen, Ananya Subburathinam, Ching-Hua Chen and Mohammed J. Zaki, Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph. In Proceedings of the 14th International Conference on Web Search and Data Mining, Virtual Event, Mar. 8-12, 2021. Acceptance rate=18.6% (112/603). [Code]
  3. [NeurIPS 2020] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. In Proceedings of the 34th Conference on Neural Information Processing Systems, Virtual Event, Dec. 6-12, 2020. Acceptance rate=20.1% (1900/9454). [PDF][Code][Slides][Video]
  4. [ISWC 2020] Nidhi Rastogi, Oshani Seneviratne, Yu Chen, Jon Harris, Diya Li, Ananya Subburathinam, Ruisi Jian, Megan Goulet, Yuheng Zhou, Osama Minhas, Jared Okun, Aaron Hill, Ching-Hua Chen and Dan Gruen, Applying Learning and Semantics for Personalized Food Recommendations. In Proceedings of the 19th International Semantic Web Conference, Virtual Event, Nov. 2-6, 2020. Demo Track paper. [PDF]
  5. [AMIA 2020] Yu Chen, Ching-Hua Chen and Mohammed J. Zaki, Combining User Preferences and Health Needs in Personalized Food Recommendation. In Proceedings of the 2020 American Medical Informatics Association Virtual Annual Symposium, Virtual Event, Nov. 14-18, 2020. [PDF]
  6. [IJCAI 2020] Yu Chen, Lingfei Wu and Mohammed J. Zaki, GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension. In Proceedings of the 29th International Joint Conference on Artificial Intelligence, Virtual Event, Jan. 7-15, 2021. Acceptance rate=12.6% (592/4717). [PDF][Code][Slides]
  7. [ICLR 2020] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation. In Proceedings of the 8th International Conference on Learning Representations, Virtual Event, Apr. 26-30, 2020. Acceptance rate=26.5% (687/2594). [PDF][Code][Slides][Video]
  8. [AAAI DLGMA 2020] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Deep Iterative and Adaptive Learning for Graph Neural Networks. In AAAI 2020 Workshop on Deep Learning on Graphs: Methodologies and Applications, New York, NY, USA, Feb. 7-12, 2020. (Best Student Paper Award). [PDF][Code][Slides]
  9. [NeurIPS GRL 2019] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model. In NeurIPS 2019 Workshop on Graph Representation Learning, Vancouver, BC, Canada, Dec. 8-14, 2019. [PDF][Code]
  10. [ISWC 2019] Steven Haussmann, Yu Chen, Oshani Seneviratne, Nidhi Rastogi, James Codella, Ching-Hua Chen, Deborah McGuinness, Mohammed J. Zaki, FoodKG Enabled Q&A Application. In Proceedings of the 18th International Semantic Web Conference, Auckland, New Zealand, Oct. 26-30, 2019. Demo Track paper. [PDF][Code]
  11. [ISWC 2019] Steven Haussmann, Oshani Seneviratne, Yu Chen, Yarden Ne’eman, James Codella, Ching-Hua Chen, Deborah L. McGuinness and Mohammed J. Zaki, FoodKG: A Semantics-Driven Knowledge Graph for Food Recommendation. In Proceedings of the 18th International Semantic Web Conference, Auckland, New Zealand, Oct. 26-30, 2019. Resources Track Full Paper. [PDF][Code][Website]
  12. [ICML LRG 2019] Yu Chen, Lingfei Wu and Mohammed J. Zaki, GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension. In ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Representations, Long Beach, CA, USA, Jun. 9-15, 2019. [PDF][Code]
  13. [NAACL 2019] Yu Chen, Lingfei Wu and Mohammed J. Zaki, Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases. In Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, MN, USA, Jun. 2-7, 2019. Long Oral Paper. Acceptance rate=26.3% (281/1067). [PDF][Code][Slides]
  14. [IEEE SSCI 2017] Yu Chen, Rhaad M. Rabbani, Aparna Gupta and Mohammed J. Zaki, Comparative Text Analytics via Topic Modeling in Banking. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, Hawaii, USA, Nov. 27-Dec. 1, 2017. [PDF][Code]
  15.  [KDD 2017] Yu Chen and Mohammed J. Zaki, KATE: K-competitive Autoencoder for Text. In Proceedings of the 23rd International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, Aug. 13-17, 2017. Full Oral PaperAcceptance rate=8.6% (64/748). [PDF][Code][Slides][Video]
  16. [CAD 2016] Yu Chen, Hao Chen and Jie Shen, Fast Voxel-based Surface Propagation Method for Outlier Removal. In Proceedings of the 13th International CAD Conference, Vancouver, BC, Canada, Jun. 27-29, 2016. [PDF][Code]

Journal Publications

  1. [IJPEM 2019] Hao Chen, Yu Chen, Xu Zhang, Baiyuan Li, Xiaoqiang Liu, Xuefei Shi and Jie Shen, A Fast Voxel-based Method for Outlier Removal in Laser Measurement. International Journal of Precision Engineering and Manufacturing, 2019. [PDF]

Patents

  1. Lingfei Wu, Yu Chen and Mohammed J. Zaki. Method and System for Subgraph Guided Knowledge Graph Question Generation. Filed, Jul. 2020.
  2. Lingfei Wu, Yu Chen and Mohammed J. Zaki. Method and System for Iterative Deep Graph Learning for Graph Neural Networks. Field, May 2020.
  3. Lingfei Wu, Yu Chen and Mohammed J. Zaki. Method and System for Natural Question Generation via Reinforcement Learning Based Graph-to-Sequence Model. Filed, Jan. 2020.
  4. Lingfei Wu, Yu Chen and Mohammed J. Zaki. Method and System for Conversational Machine Reading Comprehension via Graph Neural Networks. Filed, Aug. 2019.

Tutorials

  1. [NAACL 2021] Lingfei Wu, Yu Chen, Heng Ji and Yunyao Li. Deep Learning on Graphs for Natural Language Processing. In Proceedings of the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Jun. 6-11, 2021.

Invited Talks

  • Dissertation talk entitled "Question Answering and Generation from Structured and Unstructured Data" at RPI, Jun. 24, 2020. [Slides]
  • Invited talk entitled "Natural Question Generation and Graph Structure Learning with Graph Neural Networks" at Tencent AI Lab America, Mar. 2020.
  • Invited talk entitled "Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation" at Dataminr, Inc. and Amazon, Inc., Mar.-Apr. 2020.
  • Invited talk entitled "Automatic Graph Structure Learning for Graph Neural Networks" at IBM Research, Yorktown Heights, NY, USA, Nov. 18, 2019.
  • Invited talk entitled "Knowledge Base Question Answering and Its Potential Applications in Adaptive Education" at the 3rd International Conference on Artificial Intelligence + Adaptive Education (AIAED 2019), Beijing, China, May 24-25, 2019. [Slides]
  • Invited talk entitled "Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases" at IBM AI Horizons Seminar Series, May 2, 2019. [Talk]

Selected Awards

  • Best Student Paper Award of AAAI DLGMA 2020.
  • Student Travel Award of SIGKDD 2017.
  • 2nd Place at the 2016 Rensselaer Datathon, RPI.
  • The First-Class People’s Scholarship, UESTC, 2013, 2014.
  • National Scholarship, UESTC, 2012.

Service


Technical Program Committee


Reviewer


Old Projects

  • Evaluating countries and products in international trade. [PDF]
  • Empirical analysis of online social networks. [PDF]
  • Finding email correspondents in online social. [PDF]
  • Non-isolated and sharp featured surface outlier removal. [PDF]