Xianbin G is a recitation instructor in Computer Science at NYU Shanghai. He holds a Ph.D. of information science from University of Otago in August 2017, a Master’s degree of Economics from Shanghai Maritime University, and a Bachelor’s degree of Engineering from Wuhan University of Technology.
His research interests include machine learning, computer vision, data mining, and graph modeling. He has presented his work at a few international conferences in computer vision and data mining, such as ICIP, and PAKDD.
Before joining NYU Shanghai, he was a research assistant (part time) in Department of Information Science, University of Otago, for the projects on machine learning techniques and a lab demonstrator in Department of Computer Science, University of Otago.
Gu, X., Deng, J. D., and Purvis, M. K. (2016). A hierarchical segmentation tree for superpixel-based image segmentation. In Proceedings of the 31st International Conference on Image and Vision Computing New Zealand (IVCNZ), IEEE.
Gu, X., Purvis, M. K. (2016). Image segmentation with superpixel-based covariance descriptors. In Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Springer.
Gu, X., Deng, J. D., and Purvis M. K. (2014). Superpixel-based segmentation using multi-layer bipartite graphs and Grassmann manifolds. In Proceedings of the 29th International Conference on Image and Vision Computing New Zealand (IVCNZ), ACM.
Gu, X., Deng, J. D., and Purvis M. K. (2014). Improving superpixel-based image segmentation by incorporating color covariance matrix manifolds. In Proceedings of IEEE International Conference on Image Processing (ICIP), IEEE. (Top 10% paper award)