姓名
张志华
性别
男

所属院系
数学学院概率统计系
最高学位
博士

毕业学校
西安交通大学
博导
是

职称
教授
职务


电子邮箱
zhzhang@math.pku.edu.cn
办公室地址
理科1号楼1419E

本科生教学1
统计数据科学导论
本科生教学2
深度学习：算法与应用

本科生教学3
高维概率论
本科生教学4
强化学习：理论与算法

主要研究方向1
机器学习
研究方向2
贝叶斯统计

主要研究方向3
自然语言处理
主要研究方向4


备注
北京大学统计科学中心
北京大学大数据分析与应用技术国家工程实验室机器学习中心
教育经历
2001年 西安交通大学 博士
1994年 四川大学 硕士
1991年 成都地质学院 学士

学术成就
2018年 北京市智源学者计划
主要从事人工智能、机器学习与应用统计学领域的教学与研究，迄今在国际重要学术期刊和重要的计算机学科会议上发表 70 余篇论文。

论文及专著

专著：
IanGoodfellow，YoshuaBengio，张志华审校，《深度学习deep learning》，北京：人民邮电出版社，2017年。
Ye H., Xie G., Luo L., Zhang Z.,Fast stochastic second-order method logarithmic in condition number,Pattern Recognition,2019,88:629-642.
Luo L., Zhu W., Zhang W., Zhang T., Zhang Z., Pei J.,Sketched follow-the-regularized-leader for online factorization machine,Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2018:1900-1909.
Ye H., Li Y., Chen C., Zhang Z.,Fast Fisher discriminant analysis with randomized algorithms,Pattern Recognition,2017,72:82-92.
Ye H., Luo L., Zhang Z.,Approximate Newton methods and their local convergence,34th International Conference on Machine Learning, ICML 2017,2017,8:5981-6004.
Chen Z., Luo L., Zhang Z.,Communication lower bounds for distributed convex optimization: Partition data on features,31st AAAI Conference on Artificial Intelligence, AAAI 2017,2017:1812-1818.
Peng C., Zhang Z., Wong K.-C., Zhang X., Keyes D.E.,A scalable community detection algorithm for large graphs using stochastic block models,Intelligent Data Analysis,2017,21（6）:1463-1485.
Wang S., Zhang Z., Zhang T.,Towards more efficient SPSD matrix approximation and CUR matrix decomposition,Journal of Machine Learning Research,2016,17:1-49.
Wang S., Luo L., Zhang Z.,SPSD matrix approximation vis column selection: Theories, algorithms, and extensions,Journal of Machine Learning Research,2016,17:.
Jiang W., Xie C., Zhang Z.,Wishart mechanism for differentially private principal components analysis,30th AAAI Conference on Artificial Intelligence, AAAI 2016,2016:1730-1736.
Zhao S., Xie C., Zhang Z.,A scalable and extensible framework for superposition-structured models,30th AAAI Conference on Artificial Intelligence, AAAI 2016,2016:2372-2378.
Fu T., Luo L., Zhang Z.,Quasi-Newton hamiltonian monte carlo,32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016,2016:212-221.
Zhang Z., Li J.,Compound poisson processes, latent shrinkage priors and Bayesian nonconvex penalization,Bayesian Analysis,2015,10(2）:247-274.
Luo L., Xie Y., Zhang Z., Li W.-J.,Support matrix machines,32nd International Conference on Machine Learning, ICML 2015,2015,2:938-947.
Peng C., Zhang Z., Wong K.-C., Zhang X., Keyes D.E.,A scalable community detection algorithm for large graphs using stochastic block models,IJCAI International Joint Conference on Artificial Intelligence,2015:2090-2096.
Zhang Z.,The matrix ridge approximation: algorithms and applications,Machine Learning,2014,97(3）:227-258.
Wang S., Zhang Z.,Efficient algorithms and error analysis for the modified Nystr?m Method,Journal of Machine Learning Research,2014,33:996-1004.
Xie C., Yan L., Li W.-J., Zhang Z.,Distributed power-law graph computing: Theoretical and empirical analysis,Advances in Neural Information Processing Systems,2014,2:1673-1681.
Tu B., Zhang Z., Wang S., Qian H.,Making fisher discriminant analysis scalable,31st International Conference on Machine Learning, ICML 2014,2014,3:2614-2632.
Wang S., Tu B., Xu C., Zhang Z.,Exact Subspace clustering in linear time,Proceedings of the National Conference on Artificial Intelligence,2014,3:2113-2120.
Wang S., Zhang C., Qian H., Zhang Z.,Using the matrix ridge approximation to speedup Determinantal point processes sampling algorithms,Proceedings of the National Conference on Artificial Intelligence,2014,3:2121-2127.
Wang S., Zhang C., Qian H., Zhang Z.,Improving the modified Nystr?m method using spectral shifting,Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2014:611-620.
Zhang Z., Wang D., Dai G., Jordan M.I.,Matrix-variate dirichlet process priors with applications,Bayesian Analysis,2014,9(2）:259-286.
Zhang Z., Chen C., Dai G., Li W.-J., Yeung D.-Y.,Multicategory large margin classification methods: Hinge losses vs. coherence functions,Artificial Intelligence,2014,215:55-78.

近五年承担的主要项目
2018-2021年《大规模优化问题的近似牛顿方法：理论与实现》，国家自然科学基金面上项目（项目号：**）

社会兼职
2013~ Journal of Machine Learning Research 执行编委
美国 “数学评论” 的特邀评论员。