宋 騏

E-Mail：qisong09@ustc.edu.cn

個人主頁：https://songqi1990.github.io/


主要研究方向：數據挖掘、知識圖譜、知識計算、深度學習等


宋騏，中國科學院技術大學特任教授，入選國家高層次人才計劃（青年）。2012年和2015年於北京航空航天大學分別獲得學士與碩士學位，2020年在華盛頓州立大學獲得博士學位。2020年加入亞馬遜集團擔任應用科學家（Applied Scientist），2022年1月加入中國科學技術大學計算機學院。主要研究方向為圖數據庫及圖數據挖掘，近年來在相關領域頂級期刊及會議上發表多篇論文，包括TKDE，SIGMOD，ICDE，WWW，CIKM，ICDM，ICLR等，單篇最高引用超過1000次。


招生信息：

歡迎對數據挖掘，知識圖譜，多模態數據分析，深度學習感興趣的同學加入我們的科研小組。如果有感興趣的課題，請發送郵件至 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


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