王杰


E-Mail：jiewangx@ustc.edu.cn
个人主页：http://staff.ustc.edu.cn/~jwangx

主要研究方向：人工智能和机器学习


王杰博士在中国科学技术大学电子科学与技术系获得学士学位，在美国FloridaState University计算科学系获得博士学位。之后在美国Arizona StateUniversity和University of Michigan先后任博士后研究员和研究助理教授。现为中国科学技术大学电子工程与信息科学系特任教授，博士生导师。王杰博士在大规模机器学习算法方面的贡献受到了国内外同行的关注和认可，在国际机器学习顶级会议和期刊，如NIPS、ICML、KDD、JMLR、TPAMI、TIP、TKDD等，发表文章20余篇，曾连续三年受邀在NIPS大会上做Spotlight报告。他的一项工作也进入了由三位国际机器学习领域领军人物所撰写的教科书中。该系列工作也曾得到美国国家自然科学基金50万美元的资助。目前的主要研究方向为人工智能和机器学习，如大规模机器学习算法、深度学习、自然语言处理、强化学习、图像处理、计算机视觉等。

招生信息：
招收本科生，硕士、博士研究生和博士后来加入我的团队。候选人应具备强大的数学和/或编程技能。有意者请联系王杰并提供以下文件：目前的简历，本科和/或研究生成绩单，适用的代表性出版物。

主要论著：
[1]Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning.Tingjin Luo, Weizhong Zhang, Shuang Qiu, Yang Yang, Dongyun Yi, Guangtao Wang, Jieping Ye, and Jie Wang.SIGKDD 2017.
[2]The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands on Large-Scale Online.Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, and Jieping Ye.SIGKDD 2017.
[3]Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction.Weizhong Zhang, Bin Hong, Jieping Ye, Deng Cai, Xiaofei He, and Jie Wang.ICML 2017. [Code Download]
[4]Parallel Lasso Screening for Big Data Optimization.Qingyang Li, Shuang Qiu, Shuiwang Ji, Jieping Ye, and Jie Wang.SIGKDD 2016.
[5]A Multi-task Learning Formulation for Survival Analysis.Yan Li, Jie Wang, Jieping Ye, and Chandan Reddy.
SIGKDD 2016.
[6]Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer’s Disease Across Multiple Institutions.Qingyang Li, Tao Yang, Liang Zhan, Derrek Hibar, Neda Jahanshad, Yalin Wang, Jieping Ye, Paul Thompson, and Jie Wang.MICCAI 2016.
[7]An Efficient Algorithm For Weak Hierarchical Lasso.Yashu Liu, Jie Wang, and Jieping Ye.ACM Transactions on Knowledge Discovery from Data.
[8]New Asymptotic Analysis Method for Phase Field Models in Moving Boundary Problem with Surface Tension.
Jie Wang and Xiaoqiang Wang.Discrete and Continuous Dynamical Systems - Series B.
[9]Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection.SpotlightJie Wang and Jieping Ye.NIPS 2015.
[10]Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices.Jie Wang and Jieping Ye.ICML 2015.
[11]Detecting genetic risk factors for Alzheimer’s disease in whole genome sequence data via Lasso screening.Tao Yang, Jie Wang, Qian Sun, Derrek Paul Hibar, Neda Jahanshad, Li Liu, Yalin Wang, Liang Zhan, Paul Thompson, and Jieping Ye.IEEE International Symposium on Biomedical Imaging, 2015.
[12]Fused Lasso Screening Rules via the Monotonicity of Subdifferentials.Jie Wang, Wei Fan, and Jieping Ye.
IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear.
[13]Lasso Screening Rules via Dual Polytope Projection. (Improved version of the one accepted by NIPS 2013)
Jie Wang, Peter Wonka, and Jieping Ye.Journal of Machine Learning Research, 16(May):1063?1101, 2015. [Code Download]
[14]Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets.SpotlightJie Wang and Jieping Ye.NIPS 2014. [Code Download]
[15]A Safe Screening Rule for Sparse Logistic Regression.Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, and Jieping Ye.
NIPS 2014.
[16]An Efficient Algorithm for Weak Hierarchical Lasso.KDD'14 best student paper awardYashu Liu, Jie Wang, and Jieping Ye.SIGKDD 2014.
[17]Scaling SVM and Least Absolute Deviations via Exact Data Reduction.Jie Wang, Peter Wonka, and Jieping Ye.
ICML 2014.
[18]A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models.Jie Wang, Qingyang Li, Sen Yang, Wei Fan, Peter Wonka, and Jieping Ye.ICML 2014. [FAD Code Download]
[19]Safe Screening with Variational Inequalities and Its Application to Lasso.Jun Liu, Zheng Zhao, Jie Wang, and Jieping Ye.ICML 2014.
[20]Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods.Jie Wang, Jun Liu, and Jieping Ye.
arXiv:1307.4156v1
[21]Lasso Screening Rules via Dual Polytope Projection.Jie Wang, Jiayu Zhou, and Peter Wonka, Jieping Ye.
NIPS 2013.Spotlight
[22]An Efficient ADMM Algorithm for Multidimensional Anisotropic Total Variation Regularization Problems.
Sen Yang, Jie Wang, Wei Fan, Xiatian Zhang, Peter Wonka, and Jieping Ye.SIGKDD 2013.
[23]VCells: Simple and Efficient Superpixels Using Edge-Weighted Centroidal Voronoi Tessellations.Jie Wang, and Xiaoqiang Wang.IEEE Transactions on Pattern Analysis and Machine Intelligence
[24]Image Segmentation Using Local Variation and Edge-Weighted Centroidal Voronoi Tessellations.Jie Wang, Lili Ju, and Xiaoqiang Wang.IEEE Transactions on Image Processing
[25]Edge-Weighted Centroidal Voronoi Tessellations.Jie Wang, and Xiaoqiang Wang.Numerical Mathematics: Theory, Methods and Applications
[26]An Edge-Weighted Centroidal Voronoi Tessellation Model for Image Segmentation.Jie Wang, Lili Ju, and Xiaoqiang WangIEEE Transactions on Image Processing
[27]Evolutionary percolation model of stock market with variable agent number.Jie Wang, Chunxia Yang, Peiling Zhou, Yingdi Jin, Tao Zhou, and Binghong Wang.Physica A
[28]Epidemic Spread in Weighted Scale-Free Networks.Gang Yan, Tao Zhou, Jie Wang, Zhongqian Fu, and Binghong Wang.Chinese Physics Letters