西安电子科技大学人工智能学院导师教师师资介绍简介-刘园园

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基本信息
刘园园教授
硕导、博导
博士学科：计算机科学与技术
硕士学科：计算机科学与技术
模式识别与智能系统
工作单位：人工智能学院

联系方式
通信地址：西安电子科技大学224信箱
电子邮箱：yyliu@xidian.edu.cn
办公地点：主楼2区417

个人简介
刘园园，西安电子科技大学人工智能学院教授、硕士/博士研究生导师，智能信息处理研究所、智能感知与图像理解教育部重点实验室成员。2013年获得西安电子科技大学博士学位，并于2013年-2018年在香港中文大学从事博士后研究工作。目前的研究领域包括：机器学习、人工智能、深度学习等。分别以第一作者及通讯作者在TNNL、TPAMI、TC等顶级期刊和NIPS、AAAI、UAI、AISTATS、ICDM、SDM等顶级会议上发表学术论文30余篇。以第二完成人授权发明专利两项。曾担任IJCAI、NIPS、SDM、PR、TKDD、Journal of Optimization等国际主流会议及期刊的程序委员会委员、审稿人及客座编辑。2016年获得陕西省优秀博士学位论文奖，2018年入选华山菁英人才计划。

主要研究方向
大规模优化方法与理论大规模机器学习低秩矩阵、张量解析流形优化多任务学习

科学研究
目前承担的科研项目：
国家自然科学基金面上项目，62万，2019-2022,项目负责人.
华山菁英人才计划科研启动经费 60万元，2018-2021,项目负责人.

学术论文
代表性成果（* 通讯作者）：
Fanhua Shang, Yuanyuan Liu*, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, and Yuichi Yoshida. Guaranteed Sufficie Decrease for Stochastic Variance Reduced Gradient Optimization. In:Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018. Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, and Licheng Jiao. Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS),pp. 4875-4884, 2017.(CCF A). Yuanyuan Liu, Fanhua Shang, and James Cheng. Accelerted Variance Reduced Stochastic ADMM.In:Proceedings of the 31st AAAI Conference on Artificial Intelligence （AAAI）,pp. 2287-2293, 2017. (CCF A). Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, and Hong Cheng. Generalized Higher-Order Orthogonal Iteraton for Tensor Learning and Decomposition. IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 12, pp. 2551-2563, 2016. (IF:7.982，中科院分区1区). Yuanyuan Liu, Fanhua Shang, Licheng Jiao, James Cheng, and Hong Cheng. Trace Norm Regularized CANDECOMP/PARAFAC Decoposition with Missing Data.IEEE Transaction on Cybernetics, vol. 45, no. 11, pp. 2437-2448, 2015.(IF: 8.803,中科院分区1分区). Fanhua Shang*, Yuanyuan Liu*, James Cheng, Zhi-Quan Luo, and Zhouchen Lin. Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.(IF: 9.455,中科院分区1分区) Fanhua Shang, Yuanyuan Liu, James Cheng, and Da Yan. Fuzzy Double Trace Norm Minimization for Recommendation Systems. To appear in IEEE Transactions on Fuzzy Systems, 2017. (IF: 8.415,中科院分区1分区). Fanhua Shang,Yuanyuan Liu,and James Cheng. Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization. In:Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS),2016. Fanhua Shang,Yuanyuan Liu, James Cheng. Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization. In:Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI),2016. (CCF A). Yuanyuan Liu,Fanhua Shang,Wei Fan, James Cheng, and Hong Cheng.Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion. In: Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS),pp.1763-1771,2014. (CCF A) Fanhua Shang,Yuanyuan Liu*, and James Cheng. Generalized Higher-Order Tensor Decomposition via Parallel ADMM. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI),pp.1279-1285,2014. (CCF A) Fanhua Shang, Yuanyuan Liu*, Hanghang Tong, James Cheng, and Hong Cheng. Robust Bilinear Factorization with Missing and Grossly Corrupted Observation. Information Sciences. vol. 370, pp. 53-72, 2015.(IF: 4.305,中科院分区2分区). Yuanyuan Liu,Fanhua Shang, Hong Cheng, James Cheng,and Hanghang Tong. Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM),2014. (CCF B) Yuanyuan Liu,Fanhua Shang, Hong Cheng, and James Cheng. Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds. In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI),pp.515-524,2014. (CCF B) Yuanyua Liu, Licheng Jiao, and Fanhua Shang. An Efficient Matrix Factorization Based Low-Rank Representation for Subspace Clustering. Pattern Recognition, vol. 46, no.1, pp. 284-292, 2013.(IF: 7.197,中科院分区2分区). Yuanyuan Liu, Licheng Jiao, and Fanhua Shang. A Fast Tri-Factorization Method for Low-Rank Matrix Representation for Subspace Clustering. Pattern Recognition. vol. 46, no. 1, pp. 284-292, 2013.(IF: 3.926,中科院分区2分区). Yuanyuan Liu, Licheng Jiao, and Fanhua Shang. An Efficient Matrix Bi-Factorization Alternative Optimization Method for Trace Norm Minimization. Neural Networkds, vol. 48, pp. 8-18, 2013. (IF: 3.926,中科院分区2分区). Yuanyuan Liu,and Fanhua Shang. An Efficient Matrix Factorization Method for Tensor Completion.IEEE Signal Process. Lett.,20(4), 307-310,2013.(IF: 2.813). Fanhua Shang,Yuanyuan Liu, and Fei Wang. Learning Spectral Embedding for Semi-Supervised Clustering. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM),2011. (CCF B). Fanhua Shang, Licheng Jiao, Yuanyuan Liu, and Hanghang Tong. Semi-Supervised Learning with Nuclear Norm Regularization.Pattern Recognition,2013.(IF:3.926, 中科院分区2分区). Licheng Jiao,Fanhua Shang, Fei Wang, and Yuanyuan Liu. Fast Semi-Supervised Clustering with Enhanced Spectral Embedding.Pattern Recognition,2012.(IF:3.926,中科院分区2分区).

荣誉获奖
2016年获陕西省优秀博士学位论文奖
2018年入选华山菁英人才计划

科研团队
团队教师

博士研究生
硕士研究生

课程教学
研究生教学：模式识别
本科生教学：优化计算导论
本科生教学：科技论文写作

招生要求
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关于研究生招生的信息：
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每年计划招博士1人, 硕士4~5人(含推免保送生1～2名), 接收本校及外校保送生，优研生及统招生。若考生有下列情况之一，录取时会优先考虑：
(1) 数学和英语功底好、编程能力强的学生，录取时会优先考虑。
(2) 参加各类国家级竞赛并取得了较好的成绩。
(3) 基础好、能力强、对科研有执着追求并愿意继续攻读博士学位的学生，录取时会优先考虑。

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Research
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Team
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