Chen Wu

GitHub | Citations | Linkedin

Research Interests

My research interests include Natural Language Processing, Information Retrieval, Multimodal Machine Learning, and the application of Deep Learning to Software Engineering tasks.

Education

• Tongji University, Shanghai, China

  • Ph.D., Computer Science, 2011

  • M.S., Software Engineering, 2007

  • B.S., Computer Science, 2003

Experience

• Principal Data and Applied Scientist, Microsoft, Suzhou, China. 2022.10 – Present

  • Leverage LLM in AIOps

• Principal Research Scientist and Tech Lead, Tencent, Shanghai, China. 2021.09 – 2022.10

  • Working on Large-scale Vision-Language Model Pretraining in YouTu Lab of Tencent Cloud.

• Lead Research Scientist and Manager, Microsoft, Shanghai, China. 2020.01 – 2021.09

  • Conducting research in Semantic Code Search, Code Translation, Patch Generation, and Performance Bug Detection in Data&AI Research Team of Microsoft Developer Division.

• Staff Engineer and Manager, Alibaba, Hangzhou, China. 2016.04 – 2020.01

  • Develop models for Personalized E-Commerce Search, Machine Reading Comprehension, and Self-Supervised Pretraining in Language Technology Lab of Alibaba DAMO Academy.

• Staff Engineer and Tech Lead, Baidu, Shanghai, China. 2011.04 – 2016.04

  • Apply Machine Learning, Knowledge Graph Mining, Nature Language Processing and Ad Click Prediction to improve the performance of branding advertising in Search Advertising Team.

Technical Skills

• Programming Languages
  • Python, C++, C, C#, Java
• Deep Learning Frameworks
  • PyTorch, TensorFlow
• Others
  • Linux, LaTeX, SQL

Selected Projects

• Question Answering based on Machine Reading Comprehension Alibaba DAMO Academy
  • Designed a hierarchical attention network for reading comprehension style question answering.
  • Demo (SLQA+)
• Alibaba's Collection of Encoder-Decoders (AliceMind) Alibaba DAMO Academy
  • Provides pre-trained encoder-decoder models and its related optimization techniques.
  • Official Website
• Microsoft DeepDev Microsoft Research
  • Sharing latest innovations and deep learning models aimed at software engineering tasks.
  • Preview Website

Selected Achievements

• SOTA results in code-to-code translation on the CodeXGLUE organized by Microsoft 2021.08
• Ranked 1st on the CodeSearchNet Leaderboard by GitHub 2020.06
• Received 1st place in TREC'19 Deep Learning Track sponsored by the NIST 2019.11
• Ranked 1st on the GLUE Benchmark 2019.09
• Ranked 1st on the MS MARCO 2.0 Leaderboard hosted by Microsoft 2019.06
• Ranked 1st on the DuReader 2.0 Leaderboard hosted by Baidu 2018.10
• Ranked No.1 in the Q&A session of the TriviaQA hosted by University of Washington 2018.02
• For the first time in the history, accurate machine reading technology surpassed human reading results in the famous SQuAD Machine Reading Comprehension Competition organized by Stanford University. 2018.01
• Champion of the ACM CIKM Cup 2016 Competition 2016.10

Selected Media Coverage

• Alibaba has claimed a new record in AI language understanding MIT Technology Review , Jul 9, 2019

• Alibaba and Microsoft AI beat humans in Stanford reading test Financial Times , Jan 15, 2018

• AI Beats Humans at Reading Comprehension MIT Technology Review , Jan 15, 2018

• Computers are getting better than humans at reading CNN , Jan 16, 2018

• AI can read! Tech firms race to smarten up thinking machines AP News , Jan 27, 2018

• Alibaba's AI outperforms humans in tough reading test DailyMail , Jan 15, 2018

• Alibaba's AI Outguns Humans in Reading Test Bloomberg , Jan 14, 2018

• AI models beat humans at reading comprehension, but they've still got a ways to go The Washington Post , Jan 16, 2018

• Writing's on the wall: Alibaba AI machines beat humans at reading test The Times , Jan 16, 2018

• The Morning Download: Alibaba, Microsoft AI Bests Human Reading Comprehension The Wall Street Journal , Jan 16, 2018

Selected Talks & Presentations

• Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning, ACM CIKM 2019/11/03

• Large Scale Neural Network Language Models, Boundless：Zhejiang Lab International Youth Talents Forum 2019/08/16

• An Introduction to Machine Reading Comprehension for Question Answering, Alibaba Apsara Conference - Intelligent NLP Technology Session 2018/09/20

• Machine Reading Comprehension Technology and Applications, Alibaba Apsara Conference - NLP Technology Session 2017/10/13

• Session-aware Information Embedding for E-commerce Product Recommendation, ACM CIKM 2017/11/06

• Ensemble methods for personalized e-commerce search challenge at CIKM Cup 2016, ACM CIKM 2016/10/24

• Learning To Rank in Personalized E-Commerce Search, QCon Software Development Conference 2016/10/20

• Introduction to Query Auto-Completion, CCF YOCSEF (CCF Young Computer Scientists & Engineers Forum) 2016/06/18

Program Committees

• EMNLP 2023 (The 2023 Conference on Empirical Methods in Natural Language Processing)
• NeurIPS 2023 (Thirty-seventh Conference on Neural Information Processing Systems)
• ACL 2023 (The 61st Annual Meeting of the Association for Computational Linguistics)
• AAAI 2023 (The Thirty-Seventh AAAI Conference on Artificial Intelligence)
• COLING 2022 (The 29th International Conference on Computational Linguistics)
• IJCAI 2022 (The 31st International Joint Conference on Artificial Intelligence)
• IJCAI 2021 (The 30th International Joint Conference on Artificial Intelligence)
• COLING 2020 (The 28th International Conference on Computational Linguistics)

Patent

• Method and device for determining target page as well as equipment
  • Invertors: Chen Wu, Xiao Jiang
  • Publication Number: CN 104063394 B

Selected Publications

• Bin Bi, Chenliang Li, Chen Wu , Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si. PALM: Pre-training an Autoencoding & Autoregressive Language Model for Context-conditioned Generation. EMNLP 2020
• Wei Wang, Bin Bi, Ming Yan, Chen Wu , Zuyi Bao, Jiangnan Xia, Liwei Peng, Luo Si. StructBERT: Incorporating Language Structures into Pre-Training for Deep Language Understanding. ICLR 2020
• Bin Bi, Chen Wu , Ming Yan, Wei Wang, Jiangnan Xia, Chenliang Li. Incorporating External Knowledge into Machine Reading for Generative Question Answering. EMNLP 2019
• Jiangnan Xia, Chen Wu , Ming Yan. Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning. CIKM 2019
• Ming Yan, Jiangnan Xia, Chen Wu , Bin Bi, Zhongzhou Zhao, Ji Zhang, Luo Si, Rui Wang, Wei Wang, Haiqing Chen. A Deep Cascade Model for Multi-Document Reading Comprehension. AAAI 2019
• Wei Wang, Ming Yan, Chen Wu. Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering. ACL 2018
• Chen Wu , Ming Yan. Session-aware information embedding for e-commerce product recommendation. CIKM 2017