Dr. Jieming SHI

Assistant Professor
Department of Computing
The Hong Kong Polytechnic University

I am looking for highly self-motivated PhD students, PostDocs, and research assistants. If you are interested in working with me, please send me your CV. Thanks! (All CVs are carefully evaluated. Only matched candidates will be responded.)

Some position information here, and information about PolyU COMP.

Research Interests

Big data analytics, databases, machine learning -- especially large-scale graph algorithms, graph learning, GPU computing, and heterogeneous data management.

Email:

Short Bio

I am an assistant professor at the Department of Computing, The Hong Kong Polytechnic University (PolyU). I obtained a PhD in Computer Science from The University of Hong Kong (HKU), advised by Prof. Nikos Mamoulis and Prof. David W. Cheung, and did a postdoc at School of Computing, National University of Singapore (NUS), advised by Prof. Xiaokui XIAO. I obtained bachelor degree from Nanjing University.

News

 

• Aug 20th, 2021. Our work with NUS, NTU, HBKU, wins VLDB 2021 Best Research Paper Award!

Selected Publications (Full list, Google scholar, DBLP)
[ # Corresponding Author, ‡ Supervising Student, † Collaborating Student, * Equal Contribution]

 

• MOTS: Minimax Optimal Thompson Sampling.
  Tianyuan Jin†, Pan Xu, Jieming Shi, Xiaokui Xiao, and Quanquan Gu
  ICML 2021, in the Proceedings of the International Conference on Machine Learning, 2021.

   

• Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization.
  Tianyuan Jin, Yu Yang, Renchi Yang, Jieming Shi#, Keke Huang, and Xiaokui Xiao.
  PVLDB 2021, in the Proceedings of the VLDB Endowment, 14(10):1756-768, 2021.

   

• Effective and Scalable Clustering on Massive Attributed Graphs.
  Renchi Yang†, Jieming Shi#, Yin Yang, Keke Huang, Shiqi Zhang and Xiaokui Xiao
  TheWebConf (WWW) 2021, in the Proceedings of The Web Conference, 2021.

   

• Scaling Attributed Network Embedding to Massive Graphs. (Best Research Paper Award)
  Renchi Yang†, Jieming Shi, Xiaokui Xiao, Yin Yang, Juncheng Liu, Sourav S Bhowmick.
  PVLDB 2021, in the Proceedings of the VLDB Endowment, 2021.

   

• Multi-task Learning for Recommendation over Heterogeneous Information Network.
  Hui Li, Yanlin Wang, Ziyu Lyu, Jieming Shi.
  TKDE 2020, IEEE Transactions on Knowledge and Data Engineering, 2020.

   

• Realtime Index-Free Single Source SimRank Processing on Web-Scale Graphs.
  Jieming Shi*, Tianyuan Jin†*, Renchi Yang, Xiaokui Xiao, Yin Yang. [Code]
  PVLDB 2020, in the Proceedings of the VLDB Endowment, 2020.

   

• Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank.
  Renchi Yang†, Jieming Shi, Xiaokui Xiao, Yin Yang, Sourav S Bhowmick.
  PVLDB 2020, in the Proceedings of the VLDB Endowment, 2020.

   

• Realtime Top-k Personalized PageRank over Large Graphs on GPUs. [Technical Report]
  Jieming Shi, Renchi Yang, Tianyuan Jin, Xiaokui Xiao, Yin Yang. [Code]
  PVLDB 2020, in the Proceedings of the VLDB Endowment, 2020.

   

• Efficient Pure Exploration in Adaptive Round model.
  Tianyuan Jin†, Jieming Shi, Xiaokui Xiao, Enhong Chen.
  NeurIPS 2019, in the Proceedings of the Advances in Neural Information Processing Systems, 2019.

   

• Top-k Relevant Semantic Place Retrieval on Spatio-temporal RDF Data.
  Dingming Wu, Hao Zhou, Jieming Shi#, Nikos Mamoulis.
  VLDBJ 2019, International Journal on Very Large Data Bases, 2019.

   

• Density-based Place Clustering Using Geo-Social Network Data.
  Dingming Wu, Jieming Shi#, Nikos Mamoulis.
  TKDE 2018, IEEE Transactions on Knowledge and Data Engineering, 2018.

   

• Top-k Relevant Semantic Place Retrieval on Spatial RDF Data.
  Jieming Shi, Dingming Wu, Nikos Mamoulis.
  SIGMOD 2016, in the Proceedings of the ACM Conference on Management of Data, San Francisco, CA, June 2016.

   

• Textually Relevant Spatial Skylines.
  Jieming Shi, Dingming Wu, Nikos Mamoulis.
  TKDE 2016, IEEE Transactions on Knowledge and Data Engineering, 2016.

   

• Density-based Place Clustering in Geo-Social Networks.
  Jieming Shi, Nikos Mamoulis, Dingming Wu, David W. Cheung.
  SIGMOD 2014, in the Proceedings of the ACM Conference on Management of Data, Snowbird, UT, June 2014.

Group Members

• Yiran LI, PhD student (Fall 2020 - ), Bachelor from Nanjing University, Co-supervisor: Prof. Qing LI
• Ziang ZHOU, PhD student (Fall 2020 - ), Bachelor from Fudan University, Co-supervisor: Prof. Qing LI

Professional Service

I am/was a program committee member for the following conferences:

• IEEE International Conference on Data Engineering (ICDE), 2022
• The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021, 2022
• International Joint Conference on Artificial Intelligence (IJCAI), 2020, 2021
• ACM International Conference on Information and Knowledge Management (CIKM), 2019, 2021
• The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020, 2021
• The 5th APWeb-WAIM International Joint Conference on Web and Big Data (APWeb-WAIM), 2021
• The International Symposium on Spatial and Temporal Databases (SSTD), 2021
• IEEE International Conference on Knowledge Graph (ICKG), 2021
• IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019, 2020
• International Conference on Web Information Systems Engineering (WISE), 2019

I am/was invited reviewers for the following journals:

• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• World Wide Web: Internet and Web Information Systems (WWWJ)

Research Experiences

• 2018.7 -- 2020.6, Research Fellow, School of Computing, National University of Singapore (NUS).
• 2016.7 -- 2018.7, Advisory Researcher, Lenovo Machine Intelligence Lab, Lenovo Group Limited.
• 2012.8 -- 2012.12, Visiting Research Student, Database Group, University of New South Wales, Advisor: Prof. Xuemin Lin.
• 2011.9 -- 2015.8, Research Assistant, Data Mining and Data Management Group, The University of Hong Kong (HKU).

Useful Information

• Datasets downloadable links [click here].