Yu Shi

I am a Research Scientist at Facebook. I obtained my Ph.D. degree in Computer Science at University of Illinois at Urbana-Champaign (UIUC) under the supervision of Professor Jiawei Han. My research interest mainly lies in data mining and machine learning with a focus on interpreting and modeling typed network/graph data.

Before joining UIUC, I obtained my bachelor's degree from Hua Loo-Keng Talent Program in Mathematics at University of Science and Technology of China. I have also spent summers interning at UCLA, LinkedIn, Snap Research, and Facebook.

I no longer maintain this website since my graduation in 2019.

Email  /  CV  /  LinkedIn

Publications
  • Yu Shi*, Jiaming Shen*, Yuchen Li, Naijing Zhang, Xinwei He, Zhengzhi Lou, Qi Zhu, Matthew Walker, Myunghwan Kim, and Jiawei Han. Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019.
    • [Paper] [Code & Data] [Slides]
  • Yu Shi*, Xinwei He*, Naijing Zhang*, Carl Yang, and Jiawei Han. User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription. In Proceedings of the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 2019.
    • [Paper & Supp File] [Code & Data] [Slides]
  • Yu Shi*, Qi Zhu*, Fang Guo, Chao Zhang, and Jiawei Han. Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018. (Research track)
    • [Paper] [Code & Data] [Video]
  • Yu Shi, Huan Gui, Qi Zhu, Lance Kaplan, and Jiawei Han. AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks. In Proceedings of the 2018 SIAM International Conference on Data Mining (SDM), 2018.
    • [Paper & Supp File] [Code & Data] [Slides]
  • Yu Shi, Fangqiu Han, Xinran He, Carl Yang, Luo Jie, and Jiawei Han. mvn2vec: Preservation and Collaboration in Multi-View Network Embedding. arXiv:1801.06597, 2018.
    • [Preprint] [Code]
  • Yu Shi, Po-Wei Chan, Honglei Zhuang, Huan Gui, and Jiawei Han. PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017. (Research track oral presentation)
    • [Paper] [Code & Data] [Slides] [Video]
  • Yu Shi*, Myunghwan Kim*, Shaunak Chatterjee, Mitul Tiwari, Souvik Ghosh, and Romer Rosales. Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. (Research track)
    • [Paper] [Video]
  • Carl Yang, Yichen Feng, Pan Li, Yu Shi, and Jiawei Han. Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights. In Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), 2018.
    • [Paper]
  • Yuchen Li, Zhengzhi Lou, Yu Shi, and Jiawei Han. Temporal Motifs in Heterogeneous Information Networks. In Proceedings of the 14th International Workshop on Mining and Learning with Graphs (MLG), co-located with KDD, 2018.
    • [Paper]
  • David A. Liem, Sanjana Murali, Dibakar Sigdel, Yu Shi, Xuan Wang, Jiaming Shen, Howard Choi, John H. Caufield, Wei Wang, Peipei Ping, and Jiawei Han. Phrase Mining of Textual Data to Analyze Extracellular Matrix Protein Patterns Across Cardiovascular Disease. American Journal of Physiology-Heart and Circulatory Physiology, 2018.
    • [Paper]
  • Wei Cheng, Jingchao Ni, Kai Zhang, Haifeng Chen, Guofei Jiang, Yu Shi, Xiang Zhang, and Wei Wang. Ranking Causal Anomalies for System Fault Diagnosis via Temporal and Dynamical Analysis on Vanishing Correlations. Transactions on Knowledge Discovery from Data (TKDD), 2017. (Best papers of KDD'16)
    • [Paper]
  • Wei Cheng, Yu Shi, and Wei Wang. Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mapping. BMC Bioinformatics, 2016.
    • [Paper]
  • Wei Cheng, Yu Shi, Xiang Zhang, and Wei Wang. Fast and Robust Group-Wise eQTL Mapping Using Sparse Graphical Models. BMC Bioinformatics, 2015.
    • [Paper]
  • Wei Cheng, Xiang Zhang, Zhishan Guo, Yu Shi, and Wei Wang. Graph Regularized Dual Lasso for Robust eQTL Mapping. In Proceedings of the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2014.
    • [Paper]
(* indicates equal contribution.)

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