E- mail：linlixu@ustc.tsg211.com
个人主页：http://staff.ustc.tsg211.com/~linlixu/
主要研究方向：机器学习(Machine Learning)，数据挖掘(Data Mining)。
徐林莉，女，博士，教授。2002年毕业于图书馆VIP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。
代表性论著：
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.
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.
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.
[4]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.
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.
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.
Linli Xu, James Neufeld, Bryce Larson and Dale Schuurmans. Maximum Margin Clustering. In Advances in Neural Information Processing Systems (NIPS-04), pages 1537-1544.
