About me

Welcome to Wei-Lin Chiang’s page!

  • I am a CS PhD student in UC Berkeley Sky Computing Lab (previously RISElab), working with Prof. Ion Stoica.
  • I obtained my bachelor’s and Master’s degree from National Taiwan University under the supervision of Prof. Chih-Jen Lin.
  • My research interests include AI systems, Cloud ML, optimization for ML, and scalable ML algorithms. Currently building an intercloud broker system, SkyPilot, to bring them all together.
  • I enjoy developing open-source ML software and I am always happy to learn how they are being used! Email me if you have questions or find our projects useful.
  • More details can be found in my CV.

Work Experience

  • Intern@Amazon, Seattle (May. 2021 - Aug. 2021)
    Contrastive learning for information extraction on semi-structure webpages
  • Intern@Google Research, Mountain View (Dec. 2018 - Mar. 2019)
    Efficient algorithms for training large and deep GCN models.
    Cluster-GCN paper, code
  • Intern@Alibaba Group, Hangzhou (July 2017 - Sept. 2017)
    Distributed ML algorithms on Alibaba’s parameter server (KunPeng)
  • Intern@Microsoft Research Asia, Beijing (Dec. 2016 - Feb. 2017)
    Distributed training for deep learning frameworks
  • Intern@Microsoft, Redmond (July 2016 - Oct. 2016)
    Large-scale ML algorithms on Microsoft’s distributed platform (REEF)

Projects

  • SkyPilot (Project)
    SkyPilot is an intercloud broker system for easily and cost-effectively deploying ML workloads on any cloud
  • Balsa (Project | Paper)
    Balsa is a ML-based query optimizer, learning to optimize SQL queries by trial-and-error using deep RL and sim-to-real learning
  • Cluster-GCN (Project | Paper)
    One of the first scalable methods for training large (million-scale) and deep GCN
    Achieved state-of-the-art performance on public datasets (e.g., PPI, Reddit)
  • Distributed LIBLINEAR (Project | Paper)
    Distributed extension of a widely-used linear classification package, LIBLINEAR
    Developed L1-regularized LR solver for solving large (billion-scale) tasks.
  • Multi-core LIBLINEAR (Project | Paper)
    Multi-core extension of a widely-used linear classification package, LIBLINEAR
    Developed efficient parallel algorithms for primal and dual solvers

Publications (Google Scholar Profile)