About Me

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My Background

I was born in YangZhou (扬州), China, a beautiful historic city with a local cuisine (淮扬菜) focused on using seasonal ingredients and preparing them with delicate knife skills.

I immigrated to Canada in July, 2012 and have lived here since. I’m currently a M.Sc. student at McGill University in Montreal, Canada.

• Education:

  • M.Sc. in Computer Science, McGill University & Mila, 2022 - Present
    • Thesis: Leveraging Transformers for Spatiotemporal Modelling
    • Supervisors: Dr. Xue Liu, Dr. Alan Evans
  • B.Sc. in Computer Science and Biology, McGill University, 2018 - 2022
    • Dean’s Honour List, First Class Honours
    • Thesis: Modified Dataset for Visual Reasoning with Working Memory
    • Supervisor: Dr. Pouya Bashivan

• Experience:

  • Research Intern at McGill University & Mila (2022 - Present)
  • Undergraduate Student Researcher at McGill University (2021 - 2022)
  • Academic Services
    • Reviewer for ICLR 2025, NeurIPS 2024 Workshop on Behavioral ML, ICML 2024 Workshop on LLMs and Cognition
    • Teaching Assistant at McGill University
      • Reinforcement Learning (Winter 2023, 2024) - Taught by Dr. Doina Precup
      • Natural Language Processing (Fall 2023) - Taught by Dr. Jackie Cheung
    • Course Assistant at McGill University (2021 - 2022)
      • Discrete Structures (Fall 2021) - Taught by Dr. Jeremy Macdonald
      • Probability (Winter 2022) - Taught by Dr. Alia Sajjad
      • Linear Algebra (Winter 2022) - Taught by Dr. Henri Darmon

• Awards:

  • FRQNT Master’s Training Scholarship ($26,677)
  • NSERC Undergraduate Student Research Award with FRQNT Supplement ($9,000)
  • Quebec Programme de bourses d’excellence en sciences de l’informatique Scholarship ($1,000)
  • McGill Emily Ross Crawford & Faculty of Science Scholarships

• Besides staring at a computer screen, I enjoy bouldering, weightlifting, cooking with friends, and playing boardgames and video games. I also love listening to music when I’m coding. Here are some of my favourite albums: [].

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About the Blog

During my research, I was reading many blog posts to better understand machine learning papers, whether in the math or in the implementation details.

One that was particularly helpful to me was Lil’Log. Her intuitive explanations along with the step-by-step breakdown saved me so much time in research. As a result, many of the central ideas remain fresh in my mind.

It inspired me to start this blog to share my own insights. I hope it will help your research as well.

Thank you for stopping by and reading my blog. Feel free to connect with me!