Associate Professor (tenure-track)
John Hopcroft Center
Shanghai Jiaotong University

His research interests range across computer vision, machine learning, robotics, and data mining. I have published top-tier journal and conference papers in these four fields, which include topics of deep learning, graph theory, unsupervised learning, object detection, 3D reconstruction, 3D point cloud processing, knowledge mining, and etc.

Now, he is leading a group for explainable AI. The related topics include explainable CNNs, explainable generative networks, unsupervised semanticization of pre-trained neural networks, and unsupervised/weakly-supervised learning of neural networks. I aim to end-to-end learn interpretable models and/or unsupervisedly transform the black-box knowledge representation of pre-trained neural networks into a hierarchical and semantically interpretable model. Meanwhile, I also expect strong interpretability can ensure high transferability of features and help unsupervised/weakly-supervised learning from small data.

• Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu, "Interpretable Convolutional Neural Networks," in CVPR, 2018

• Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, and Song-Chun Zhu, "Interpreting CNN Knowledge via an Explanatory Graph," in AAAI, 2018

• Quanshi Zhang, Wenguan Wang, and Song-Chun Zhu, "Examining CNN Representations with respect to Dataset Bias," in AAAI, 2018

• Quanshi Zhang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu, "Mining Object Parts from CNNs via Active Question-Answering," in CVPR 2017

• Quanshi Zhang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu, "Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning," in AAAI 2017

• Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki, "Object Discovery: Soft Attributed Graph Mining," in IEEE Transactions on PAMI 38(3):532-545, 2016.