宋 骐

时间：2022-02-20浏览：3583

E-Mail：qisong09@ustc.edu.cn

个人主页：https://songqi1990.github.io/


主要研究方向：数据挖掘、知识图谱、知识计算、深度学习等


宋骐，中国科学院技术大学特任教授，入选国家高层次人才计划（青年）。2012年和2015年于北京航空航天大学分别获得学士与硕士学位，2020年在华盛顿州立大学获得博士学位。2020年加入亚马逊集团担任应用科学家（Applied Scientist），2022年1月加入中国科学技术大学计算机学院。主要研究方向为图数据库及图数据挖掘，近年来在相关领域顶级期刊及会议上发表多篇论文，包括TKDE，SIGMOD，ICDE，WWW，CIKM，ICDM，ICLR等，单篇最高引用超过1000次。


招生信息：

欢迎对数据挖掘，知识图谱，多模态数据分析，深度学习感兴趣的同学加入我们的科研小组。如果有感兴趣的课题，请发送邮件至 qisong09@ustc.edu.cn 。


代表性著作：

  1. Qi Song*, Mohammad Hossein Namaki, Peng Lin, and Yinghui Wu, Answering Why-Questions for Subgraph Queries;  IEEE Transactions on Knowledge and Data Engineering(TKDE), Vol.34 (Issue No. 10), 4636-4649, 2022   

  2. Qi Song*, Peng Lin, Hanchao Ma, Yinghui Wu, Explaining Missing Data in Graphs: A Constraint-based Approach; IEEE 36th International Conference on Data Engineering (ICDE), 2021, pp. 1476-1487  

  3. Qi Song*, Yinghui Wu, Peng Lin, Luna Xin Dong, Hui Sun, Mining summaries for knowledge graph search; IEEE Transactions on Knowledge and Data Engineering(TKDE) 30 (10), 1887-1900

  4. Qi Song#, Mohammad Hossein Namaki#, Yinghui Wu, Answering Why-Questions for Subgraph Queries in Multi-Attributed Graphs; IEEE 35th International Conference on Data Engineering (ICDE), 2019, pp. 40-51

  5. Mohammad Hossein Namaki#, Qi Song#, Yinghui Wu, Shengqi Yang Answering why-questions by exemplars in attributed graphs; Proceedings of the 2019 International Conference on Management of Data (SIGMOD), 2019, pp. 1481-1498

  6. Qi Song*, Bo Zong, Yinghui Wu, Lu-An Tang, Hui Zhang, Guofei Jiang, Haifeng Chen, TGNet: Learning to rank nodes in temporal graphs; Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), 2018, pp. 97-106

  7. Qi Song*, Yinghui Wu, Luna Xin Dong,    Mining Summaries for Knowledge Graph Search; IEEE 16th International Conference on Data Mining (ICDM), 2016, pp. 1215-1220

  8. Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen, Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. International Conference on Learning Representations(ICLR) 2018