|
招机器学习文本挖掘方向2024年入学博士生,常年招文本挖掘、图机器学习方向博士后和本科实习生。联系email : mzhang_cs [at] pku.edu.cn 找大学、找方向和找导师可以参考CS排名(CSRankings高水平计算机学术会议论文排名): 基于DBLP的全球计算机专业大学和教授排名,有教授主页链接和谷歌学术网页Short Bio张铭,北京大学计算机学院教授,博士生导师,教育部计算机课程教指委委员;ACM China常务理事,中国ACM教育专委会主席,ACM/IEEE IT2017信息技术课程指南执委,ACM/IEEE CC2020计算机学科规范领导小组成员,2013-2021任ACM全球教育指导委员会唯一的中国理事。自1984年考入北京大学,分别获得学士、硕士和博士学位。研究方向为招文本挖掘、图机器学习等,目前主持国家自然科学基金面上项目以及公司合作项目,合作发表科研学术论文300多篇(NeurIPS, ICLR, ICML, KDD, WWW, ACL, AAAI, IJCAI, TKDE等A类会议和期刊),获得机器学习顶级会议ICML 2014最佳论文奖、网络信息处理顶级会议WWW 2016最佳论文提名、数据挖掘顶级会议ICDM 2022最佳论文提名。入选“全球2000位最具影响力AI学者”,主要贡献为信息检索与推荐领域。 发表了SIGCSE、L@S等教学研究论文,出版学术专著1部,获软件著作权8项,获发明专利6项。主编多部教材,其中《数据结构与算法》获北京市精品教材奖并得到国家“十二五”规划教材支持。带领北大《数据结构与算法》团队,获评国家级和北京市级精品课程(2008)、国家级精品资源共享课程(2016)、国家精品在线课程(2018、2020年首批线上一流课程)、首批国家级一流本科课程(线上线下混合式一流课程),都排名第一位。2021年荣获CCF杰出教育奖。2022年获评CCF杰出演讲者。
Ming Zhang received her Bachelor, master and PhD degrees in Computer Science from Peking University respectively. She is a full professor at the School of Computer Science, Peking University. Prof. Zhang is a member of Advisory Committee of Ministry of Education in China, a member at large of ACM China Chapter, a former member of ACM Education Advisory Committee (2013-2021) and the Chair of ACM SIGCSE China. She is one of the five Executive Committee Members of ACM/IEEE IT2017 and a member of the ACM/IEEE CC2020 steering group. She has published more than 200 research papers on Text Mining and Machine Learning in the top journals and conferences, such as ICML, NeurIPS, KDD, WWW, ACL, AAAI, IJCAI and TKDE. She won the best paper of ICML 2014 and best paper nominee of WWW 2016. Prof. Zhang is the leading author of several textbooks on Data Structures and Algorithms in Chinese, and the corresponding course is awarded as the National Elaborate Course, National Boutique Resource Sharing Course, National Fine-designed Online Course, National First Class Course by MOE China.
Wei Ju, Yifang Qin, Ziyue Qiao, Xiao Luo, Yifan Wang, Yanjie Fu, Ming Zhang: Kernel-based Substructure Exploration for Next POI Recommendation. ICDM 2022最佳论文提名
Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, Qun Liu: Generate & Rank: A Multi-task Framework for Math Word Problems. EMNLP (Findings) 2021: 2269-2279
Kewei Cheng, Ziqing Yang, Ming Zhang, Yizhou Sun: UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference. EMNLP (1) 2021: 9753-9771
A Clear, A Parrish, J Impagliazzo, P Wang, P Ciancarini, E Cuadros-Vargas, S Frezza, J Gal-Ezer, A Pears, S Takada, H Topi, G van der Veer, A Vichare, L Waguespack, M Zhang: Computing curricula 2020 (CC2020) paradigms for global computing education[中文]. ACM/IEEE
Yifan Wang, Suyao Tang, Yuntong Lei, Weiping Song, Sheng Wang, Ming Zhang: DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation. CIKM 2020: 1605-1614
Yiping Song, Zequn Liu, Wei Bi, Rui Yan, Ming Zhang,Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks,ACL 2020: 5832-5841. [源码] [论文简介][视频]
Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang,A Graph to Graphs Framework for Retrosynthesis Prediction,Accepted by ICML 2020 [论文简介][视频]
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang,GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation,ICLR 2020 [论文简介][视频]
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang,AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks,CIKM 2019: 1161-1170
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang,Session-Based Social Recommendation via Dynamic Graph Attention Networks,WSDM 2019: 555-563
Ming Zhang, Bo Yang, Steve Cooper, Andrew Luxton-Reilly: Proceedings of the ACM Conference on Global Computing Education, CompEd 2019, Chengdu,Sichuan, China, May 17-19, 2019. ACM 2019, ISBN 978-1-4503-6259-7
Ming Zhang; Jile Zhu; Zhuo Wang; Yunfan Chen,Providing personalized learning guidance in MOOCs by multi-source data analysis,World Wide Web 22(3): 1189-1219 (2019)
Yiping Song, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao, Rui Yan. An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems. IJCAI 2018: 4382-4388.
Luchen Liu, Jianhao Shen, Ming Zhang, Zichang Wang and Jian Tang. Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction. AAAI’18 [源码] [论文简介]
Ming Zhang; Jile Zhu,A data-driven analysis of student efforts and improvements on a SPOC experiment, ACM TUR-C 2017: 1:1-1:6
Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han, An Attention-based Collaboration Framework for Multi-View Network Representation Learning, CIKM 2017: 1767-1776
He Jiang ; Yangqiu Song; Chenguang Wang; Ming Zhang; Yizhou Sun,Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks, IJCAI 2017: 1944-1950
Xiang Li, Lili Mou, RuiYan, Ming Zhang. StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation. IJCAI 2016: 2845-2851.《每日邮报》报道;北大新闻报道“信息科学技术学院张铭教授课题组在人机对话系统研究中取得重要进展”
Yin Yichun, Wei Furu, Dong Li, Xu Kaimeng, Zhang Ming and Zhou Ming. Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction. IJCAI 2016: 2979-2985.
Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei. Visualizing Large-scale and High-dimensional Data. WWW 2016: 287-297. (网络信息领域顶会 Best paper nominee).
Chenguang Wang, Yangqiu Song, Haoran Li, Ming Zhang, Jiawei Han. Text Classification with Heterogeneous Information Network Kernels. AAAI 2016: 2130-2136. 数据下载
Chenguang Wang, Yangqiu Song, Ahmed El-Kishky, Dan Roth, Ming Zhang, Jiawei Han, Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks. SIGKDD 2015:1215-1224.
Ming Zhang, Jile Zhu, Yanzhen Zou, Hongfei Yan, Dan Hao, Chuxiong Liu: Educational Evaluation in the PKU SPOC Course "Data Structures and Algorithms". In Proc. of ACM Conference on Learning at Scale , L@S 2015: 237-240
Chenguang Wang, Yangqiu Song, Dan Roth, Chi Wang, Jiawei Han, Heng Ji, Ming Zhang. Constrained Information-Theoretic Tripartite Graph Clustering to Identify Semantically Similar Relations. IJCAI 2015: 3882-3889
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei. LINE: Large-scale Information Network Embedding. WWW 2015: 1067-1077.源代码下载
Fangtao Li, Sheng Wang, Shenghua Liu, Ming Zhang. SUIT: A Supervised User-Item based Topic model for Sentiment Analysis. In Proc. of Twenty-Eighth Conference on Artificial Intelligence (AAAI-14), 2014:1636-1642.
Ziqi Wang, Gu Xu, Hang Li, and Ming Zhang, A Probabilistic Approach to String Transformation. IEEE Trans. Knowl. Data Eng. 26(5): 1063-1075 (2014).
Jian Tang, Zhaoshi Meng, XuanLong Nguyen, Qiaozhu Mei, Ming Zhang. Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis. ICML 2014: 190-198 (机器学习顶会 Best Paper)
Jian Tang, Ming Zhang, and Qiaozhu Mei. One Theme in All Views: Modeling Consensus Topics in Multiple Contexts. KDD 2013, Oral full paper. Chicago, U.S, August 11-14, 2013. PP5-13..
Tao Sun, Ming Zhang, and Qiaozhu Mei. Unexpected Relevance: An Empirical Study of Serendipity in Retweets. In The 7th International AAAIConference on Weblogs and Social Media, 2013.
Xiaolong Wang, Furu Wei, Xiaohua Liu, Ming Zhou and Ming Zhang. Graph-based Sentiment Classification for Hashtags in Twitter. CIKM 2011, Glasgow, UK. PP1031-1040.
Ziqi Wang, Gu Xu, Hang Li and Ming Zhang, A Fast and Accurate Method for Approximate String Search. The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL’11), Portland, Oregon, USA, June 19-24, 2011. PP52-61.
Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang and Edward Chang. PFP: Parallel FP-Growth for Query Recommendation. In ACM Recommender Systems 2008. October 23-25, 2008 Polydôme Lausanne Switzerland. Pages 107-114. PFP has been made part of Apache Mahout (http://mahout.apache.org/users/misc/parallel-frequent-pattern-mining.html).
张铭,陈娟,韩飞,杨晓春,吴锡,"ACM/IEEE计算课程体系规范CC2020对中国计算机专业设置的启发"。《中国计算机学会通讯》,2020年,第16卷第12期,32-37
张铭,“计算机教育的研究”。《中国计算机学会通讯》.2019年,第15卷第4期.8-9
张科, 张铭, 陈娟, 张昱, 常轶松,“计算机教育研究浅析——从ACM计算机科学教育大会看国内外计算机教育科研”。《中国计算机学会通讯》.2019年,第15卷第4期.16-25
张铭,“计算机教育的科学研究和展望”。《计算机教育》.2017年第12期,12.5-10
张昱、陈娟、肖胜刚、张铭,“由第 48 届 ACM 计算机科学教育大会看国内计算机教育科研”。《计算机教育》.2017年第9期,176-179
张铭,银平,邓志鸿,杨冬青,“SVM+BiHMM:基于统计方法的元数据自动抽取混合模型”。《软件学报》.19(2).358-368,2008年2月
邓志鸿,唐世渭,张铭,杨冬青,陈捷。“Ontology研究综述”。《北京大学学报》(自然科学),2002年9月,第38卷5期:730-738。
Ming Zhang, Virginia Mary Lo: Undergraduate computer science education in China. In Gary Lewandowskiet. al. (Eds.): Proceedings of the 41st ACM technical symposium on Computer science education, SIGCSE 2010, Milwaukee, Wisconsin, USA, March 10-13, 2010. ACM 2009, ISBN 978-1-4503-0006-3 pp. 396-400.A referred paper by the course "Images of Computing" delivered by Dr. Carol Frieze, http://www.cs.cmu.edu/~cfrieze/courses/
张铭,耿国华,陈卫卫,胡学刚. 数据结构与算法课程教学实施方案[J]。中国大学教学,2011(3):PP56-60.
张铭,赵海燕,王腾蛟,宋国杰,《数据结构与算法实验教程》,高等教育出版社,2011年1月。普通高等教育“十一五”国家级规划教材。
张铭、王腾蛟、赵海燕,《数据结构与算法》,高等教育出版社,2008年 6月。普通高等教育“十一五”国家级规划教材
张铭、赵海燕、王腾蛟,《数据结构与算法--学习指导与习题解析》,高等教育出版社,2005年 10月。普通高等教育“十五”国家级规划教材配套参考书
李晓明、陈平、张铭、朱敏悦。“关于计算机人才需求的调研报告”。《计算机教育》,2004年8月,PP11-18。
主要荣誉与获奖
2002年 北京大学2001-2002学年教学优秀奖
2004年“数据结构与算法课程的教学研究和实践”北京大学教学成果一等奖(排名一)
2008年 “数据结构与算法”获得国家级和北京市级精品课程(排名一)
2011年 《数据结构与算法》获得北京市优秀教材奖(排名一)
2013年 “数据结构与算法”获北京市微课比赛优秀作品奖(排名一)
2013年 北京大学“杨芙清-王阳元院士教师奖”优秀奖
2014年 机器学习领域顶会ICML最佳论文奖
2016年 “数据结构与算法”获评教育部精品资源共享课程(排名一)
2016年 “数据结构与算法”获中国高校计算机教育MOOC联盟优秀课程奖(排名一)
2017年“普惠中拔尖”,获北京市高等教育教学成果一等奖(排名三)
2017年“计算思维创新教学实践:数据结构与算法”,获北京市高等教育教学成果一等奖(排名二)
2018年 北京大学优秀工会积极分子
2018年“数据结构与算法”被评为国家精品在线开放课程(排名一)
2020年“数据结构与算法”(线上)被评为国家精品在线开放课程(排名一)
2020年“数据结构与算法”(线上线下混合式)国家级一流本科课程(排名一)
2021年 CCF杰出教育奖
2022年 北京大学2022年度曾宪梓优秀教学奖
主要乒乓球获奖
Last Updated: March 2023 |