Wenhu Chen is a Canada CIFAR AI Chair at the Vector Institute. His research mainly focuses on natural language processing, deep learning and multimodal learning. He designs models and algorithms that can make current AI models more grounded and trustworthy. More specifically, he designs approaches to incorporate world knowledge into different deep neural networks, helping them make better and more transparent predictions.
- Outstanding Dissertation Award, University of California, Santa Barbara, June 2021
- WACV 2021 Best Student Paper Honorable Mention, Jan 2021
- Tencent AI PhD Gift Award, University of California, Santa Barbara, June 2018
- IDEA Exchange Grant, RWTH Aachen University, June 2016
- Wenhu Chen, Yu Su, Xifeng Yan, and William Yang Wang. (2020). KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8635–8648, Online. Association for Computational Linguistics.
- Chen, W., Chang, M., Schlinger, E., Wang, W.Y., & Cohen, W.W. (2021). Open Question Answering over Tables and Text. ArXiv, abs/2010.10439.
- Chen, W., Xiong, W., Yan, X., & Wang, W.Y. (2018). Variational Knowledge Graph Reasoning. NAACL.
- Chen, W., Gan, Z., Li, L., Cheng, Y., Wang, W.Y., & Liu, J. (2021). Meta Module Network for Compositional Visual Reasoning. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 655-664.
- Chen, W., Hu, H., Chen, X., Verga, P., & Cohen, W.W. (2022). MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.