Bin Wu

Bin Wu

Research Scientist

Database and Storage Lab, Alibaba Cloud

Biography

Interests
  • OLAP algorithms and systems
  • Query optimization
  • DB for machine learning
  • AI for large-scale systems
Education
  • Ph.D in Computer Science, 2017

    The Hong Kong University of Science and Technology(HKUST)

  • B.S. in Computer Science, 2012

    Fudan University

Who am I?

Bin Wu currently is a Research Scientist at Database and Storage Lab, Alibaba Cloud. Before that, he is a researcher at Noah’s Ark Lab of Huawei. His research interest lies in the cross-intersection of theoretical computer science, large-scale database management systems and machine learning. He received his Ph.D in Computer Science from HKSUT under the supervision of Prof. Ke Yi in 2017 and received his bachelor’s degree from Fudan University in 2012. He is a recipient of ACM SIGMOD Research Highlight Award(2017) and ACM SIGMOD Best Paper Award(2016).

    Skills

    Technical
    Python
    C/C++
    JAVA
    Hobbies
    Soccer
    Crossfit
    Motorcycle

    Experience

     
     
     
     
     
    Alibaba Cloud, Alibaba Group
    Research Scientist
    Alibaba Cloud, Alibaba Group
    December 2018 – Present Hangzhou, China
    Research on DB4AI & AI4DB
     
     
     
     
     
    Noah’s Ark Lab, Huawei
    Researcher
    Noah’s Ark Lab, Huawei
    August 2017 – October 2018 Hong Kong, China
    Conducted research on Meta-learning and few-shot learning related topics and resolved some real world problems with existing meta learning methods.

    Accomplish­ments

    ACM SIGMOD Research Highlight Award(2017)
    ACM SIGMOD Best Paper Award(2016)

    Projects

    Publications

    Visit Google Scholar for the fulllist. Quickly discover relevant content by filtering publications.
    Anser: Adaptive Information Sharing Framework of AnalyticDB

    CatSQL: Towards Real World Natural Language to SQL Applications

    Learning-based query optimization for multi-probe approximate nearest neighbor search

    OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting