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Biography

My name is Jin-Siang Lin, a graduate student with Computer Science background.

Currently, I am working at Synopys as a R&D.

I'm responsible for developing API for Verdi debug tool.


Email Github Linkedin CV

Work Experience


Synopys, Hsinchu, Taiwan
Verdi R&D
  • Date : 11/2022 - now
  • Responsible for API development for Verdi debug tool.

  • MediaTek, Hsinchu, Taiwan
    GPU software engineer
  • Date : 09/2021 - 10/2022
  • GPU driver and application development, especially on Vulkan API.
  • Responsible for Vulkan layers and tools development for GPU performance tuning.

  • Qualcomm, Hsinchu, Taiwan
    Machine learning engineer (intern)
  • Date : 04/2021 - 07/2021
  • Responsible for deployment of machine learning flow for semiconductor manufacturing data.
  • Optimize the original data-pipeline for 43x faster.


  • Education


    National Tsing Hua University
  • Master of Science, Electrical Engineering in Vision Science Lab
  • Advisor : MIN SUN
  • Concentration: Deep learning, especially on Reinforcement learning

  • National Tsing Hua University
  • Bachelor of Science, Computer Science
  • GPA : 3.7 / 4.3


  • Research Experience


    Policy Transfer / Policy Composition on Reinforcement learning
  • Date : 10/2019 - 08/2020
  • Our research focuses on the policy transfer on robotic simulation and application
  • We come up with a good way to decompose the behaviors of policy on pre-trained tasks into some sub-primitive policies
  • We further leverage these sub-primitives to increase the sample efficiency on transfer tasks

  • Medical image colorization
  • Date : 08/2020 - 03/2021
  • My new project focus on Medcial image colorization (e.g. CT image) using Deep Learning methodology.


  • Publication


    Hierarchical Alternative Training for Long Range Policy Transfer
  • ICML 2020 BIG workshop
  • Second Author
  • We proposed a framework called Hierarchical Alternative Training(HAT) that leverages the hierarchical structure to train the combination function and adapt the primitive polices alternatively, to efficiently produce a range of complex behaviors on challenging new tasks.

  • Toward Robust Long Range Policy Transfer
  • AAAI 2021
  • Second Author
  • We propose a method, which leverages the hierarchical structure to train the combination function and adapt the set of diverse primitive polices alternatively, to efficiently produce a range of complex behaviors on challenging new tasks.