cv

Yinhan Lu's Curriculum Vitae

Education

  • 2023-2026(Expected)
    Bachelor of Science, Honour Computer Science
    McGill University, Montreal, Quebec
    • GPA: 3.93/4.00 | Computer Science Courses: 4.00/4.00
    • Dean's Honour List (2024-2025)

Experience

  • Sept 2025 - Present
    Student Researcher
    McGill University & Mila - Quebec AI Institute, Montreal, QC
    Mentor: Eric Elmoznino
    • Working on developing and benchmarking a minimally-modified causal sequence model architectures.
  • Aug 2025 - Present
    Research Intern
    McGill University & Mila - Quebec AI Institute, Montreal, QC
    Supervisor: David Adelani
    • Working on enhancing translation in low resources language without enough parallel corpus.
  • March 2025 - July 2025
    Researcher
    McGill AI Society Research Incubator, Montreal, QC
    • Explored bias detection in parliamentary discourse by training RoBERTa models from scratch and analyzing embedding space geometry across Inuit and Canadian Parliament datasets.
  • May 2024 - Present
    Research Intern
    Université de Montréal & Mila - Quebec AI Institute, Montreal, QC
    Supervisor: Bang Liu | Mentor: Haochen Shi
    • Designed and implemented modular LLM-based agent systems for autonomous exploration in open-world environments, investigating self-supervised learning approaches to improve reasoning and planning capabilities.
  • Jun 2024 - Aug 2024
    Research Intern
    National Taiwan University, HCI Lab, Taipei, Taiwan
    Supervisor: Mike Chen | Mentor: Wei-Chung Su & Shun-Yu Wang
    • Contributed to RoomDreaming, an AI-assisted iterative design system.

Courses

  • Graduate Courses (500-level)
    • COMP 551: Applied Machine Learning
    • COMP 549: Brain-Inspired AI
    • COMP 579: Reinforcement Learning
    • COMP 545: Natural Language Understanding with Deep Learning
    • COMP 520: Compiler Design (Expected)
  • Undergraduate Computer Science
    • COMP 202: Foundations of Programming
    • COMP 206: Introduction to Software Systems
    • COMP 250: Introduction to Computer Science
    • COMP 252: Honours Algorithms & Data Structures
    • COMP 273: Introduction to Computer Systems
    • COMP 302: Programming Languages & Paradigms
    • COMP 303: Software Design (Expected)
    • COMP 308: Computer Systems Lab (Expected)
    • COMP 310: Operating Systems
    • COMP 330: Theory of Computation (Expected)
    • COMP 350: Numerical Computing
    • COMP 360: Algorithm Design (Expected)
    • COMP 400: Project in Computer Science
  • Mathematics
    • MATH 133: Linear Algebra and Geometry
    • MATH 140: Calculus 1
    • MATH 141: Calculus 2
    • MATH 208: Introduction to Statistical Computing
    • MATH 222: Calculus 3
    • MATH 240: Discrete Structures
    • MATH 247: Honours Applied Linear Algebra
    • MATH 323: Probability
    • MATH 324: Statistics
    • MATH 340: Discrete Mathematics
  • Other
    • BIOL 112: Cell and Molecular Biology (Expected)

Technical Skills

  • Programming Languages
    • Python, C, C++, Java, JavaScript, TypeScript, R, Bash, OCaml, MATLAB
  • ML/AI
    • PyTorch, Transformers, Gym, scikit-learn, NumPy, Pandas, Matplotlib
  • Tools & Technologies
    • Git, Docker, Jupyter, Hugging Face, Weights & Biases, React.js, Unix/Linux, LaTeX, Figma