个人主页：http://staff.ustc.edu.cn/~linlixu/ 

E-mail：linlixu@ustc.edu.cn 

主要研究方向： 机器学习，数据挖掘，聚类算法，非监督学习以及半监督学习，支持向量机及其相关的扩展，凸优化算法在机器学习中的应用等。

徐林莉，女，博士，教授。2002年毕业于中国科学技术大学计算机科学与技术系，获学士学位；2007年于加拿大滑铁卢大学(University of Waterloo)计算机学院获得博士学位。研究着重于从复杂的数据中学习有价值的信息，利用数学建模发展相应的算法。研究课题包括各种聚类(Clustering)算法，非监督学习(Unsupervised Learning)以及半监督学习(Semi-supervised Learning)，支持向量机(Support Vector Machines)及其相关的扩展，凸优化算法(Convex Programming)在机器学习中的应用等。在人工智能/机器学习领域顶级国际会议中发表论文多篇。

获 奖 情 况：

ICML2009年度最佳论文优秀奖。

主要论著：

[01]  Linli Xu, Wenjun Ouyang, Yang Wang, Xiaoying Ren, Liang Jiang, Enhancing Semantic Representations of Bilingual Word Embeddings with Syntactic Dependencies. To appear in Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'2018).

[02]  Junliang Guo, Linli Xu, Xunpeng Huang, Enhong Chen, Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective. To appear in Proceedings of the 23rd International Conference on Database Systems for Advanced Applications (DASFAA'2018).

[03]  Linli Xu, Liang Jiang, Chuan Qin, Zhe Wang, Dongfang Du, How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks. In Proceedings of the 31th AAAI Conference on Artificial Intelligence (AAAI'2017).

[04]  Linli Xu, Chao Zhang, Bridging Video Content and Comments: Synchronized Video Description with Temporal Summarization of Crowdsourced Time-sync Comments. In Proceedings of the 31th AAAI Conference on Artificial Intelligence (AAAI'2017).

[05]  Linli Xu, Zaiyi Chen, Qi Zhou, Enhong Chen, Nicholas Jing Yuan, Xing Xie, Aligned Matrix Completion: Integrating Consistency and Independency in Multiple Domains, The 16th IEEE International Conference on Data Mining (ICDM'2016).

[06]  Linli Xu, Qi Zhou, Aiqing Huang, Wenjun Ouyang, Enhong Chen, Feature Selection with Integrated Relevance and Redundancy Optimization, The 15th IEEE International Conference on Data Mining (ICDM'2015): 1063-1068, Atlantic City, NJ, USA, November 14-17, 2015.

[07]  Liyuan Liu, Linli Xu*, Zhen Wang and Enhong Chen, Community Detection Based on Structure and Content: A Content Propagation Perspective, The 15th IEEE International Conference on Data Mining (ICDM'2015): 271-280, Atlantic City, NJ, USA, November 14-17, 2015.

[08]  Yingzi Wang, Nicholas Jing Yuan, Defu Lian, Linli Xu, Xing Xie and Yong Rui. Regularity and Conformity: Location Prediction Using Heterogeneous Mobility Data. In Proceedings of the 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-15).

[09]      Yitan Li, Linli Xu, Fei Tian, Liang Jiang, Xiaowei Zhong and Enhong Chen. Word Embedding Revisited: A New Representation Learning and Explicit Matrix Factorization Perspective. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15).

[10]  Zaiyi Chen, Linli Xu and Enhong Chen. Selecting Social Media Responses to News: A Convex Framework Based On Data Reconstruction. In Proceedings of the 2015 SIAM International Conference on Data Mining (SDM-15).

[11]  Linli Xu, Aiqing Huang, Jianhui Chen and Enhong Chen. Exploiting Task-Feature Co-Clusters in Multi-Task Learning. In Proceedings of the 29th National Conference on Artificial Intelligence (AAAI-15).

[12]  Xiaowei Zhong, Linli Xu, Yitan Li, Zhiyuan Liu and Enhong Chen. A Nonconvex Relaxation Approach for Rank Minimization Problems. In Proceedings of the 29th National Conference on Artificial Intelligence (AAAI-15).

[13]  Linli Xu, Yitan Li, Yubo Wang and Enhong Chen. Temporally Adaptive Restricted Boltzmann Machine for Background Modeling. In Proceedings of the 29th National Conference on Artificial Intelligence (AAAI-15).

[14]  Linli Xu, Zhen Wang, Zefan Shen, Yubo Wang and Enhong Chen. Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels. In Proceedings of the 14th IEEE Conference on Data Mining (ICDM-14).

[15]  Aiqing Huang, Linli Xu, Yitan Li and Enhong Chen. Robust Dynamic Trajectory Regression on Road Networks: A Multi-Task Learning Framework. In Proceedings of the 14th IEEE Conference on Data Mining (ICDM-14).

[16]  Yubo Wang, Linli Xu, Yucheng Chen and Hao Wang. A Scalable Approach for General Correlation Clustering. In the 9th International Conference on Advanced Data Mining and Applications (ADMA-13).

[17]  Tianbao Yang, Prakash Mandayam Comar, Linli Xu. Community Detection by Popularity Based Models for Authored Networked Data. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM-13).

[18]  Linli Xu, Bo Li, Enhong Chen. Ensemble Pruning via Constrained Eigen-Optimization. In Proceedings of the IEEE International Conference on Data Mining (ICDM-12).

[19]  Le Wu, Enhong Chen, Qi Liu, Linli Xu, Tengfei Bao, Lei Zhang. Leveraging Tagging for Neighborhood-aware Probabilistic Matrix Factorization. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM-12).

[20]  Haiping Ma, Enhong Chen, Hui Xiong and Linli Xu. Capturing Correlations of Multiple Labels: A Generative Probabilistic Model for Multi-label Learning. In Neurocomputing 2011.

[21]  Jiming Peng, Lopamudra Mukherjee, Vikas Singh, Dale Schuurmans and Linli Xu. An Efficient Algorithm for Maximal Margin Clustering. In Journal of Global Optimization 2011.

[22]  Yaoliang Yu, Min Yang, Linli Xu, Martha White and Dale Schuurmans. Relaxed Clipping: A Global Training Method for Robust Regression and Classification. In Advances in Neural Information Processing Systems (NIPS-10).

[23]  Linli Xu, Martha White and Dale Schuurmans. Optimal Reverse Prediction: A Unified Perspective on Supervised, Unsupervised and Semi-supervised Learning. In Proceedings of the 26th International Conference on Machine Learning (ICML-09), pages 1137-1144. Best Paper Award Honorable Mention.

[24]  Linli Xu, Wenye Li and Dale Schuurmans. Fast Normalized Cut with Linear Constraints. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR-09), pages 2866-2873.

[25]  Linli Xu. Convex Large Margin Training Techniques for Unsupervised, Semi-supervised, and Robust Support Vector Machines. Ph.D. Thesis, School of Computer Science, University of Waterloo, 2007.

[26]  Linli Xu, Koby Crammer and Dale Schuurmans. Robust Support Vector Machine Training via Convex Outlier Ablation. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-06), pages 536-542.

[27]  Linli Xu, Dana Wilkinson, Finnegan Southey and Dale Schuurmans. Discriminative Unsupervised Learning of Structured Predictors. In Proceedings of the 23rd International Conference on Machine Learning (ICML-06), pages 1057-1064.

[28]  Linli Xu and Dale Schuurmans. Unsupervised and Semi-supervised Multi-class Support Vector Machines. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI-05), pages 904-910.

[29]  Linli Xu, James Neufeld, Bryce Larson and Dale Schuurmans. Maximum Margin Clustering. In Advances in Neural Information Processing Systems (NIPS-04), pages 1537-1544.