Curious about mathematics, physics, and computer science.
I am an 8th grader from Shanghai who enjoys building projects, studying fundamentals, and connecting ideas across disciplines.
I am especially interested in the intersection of mathematics, physics, and computer science.
For me, mathematics is the language that describes structure, physics is where that structure starts to feel intuitive, and computer science is where those ideas become systems you can actually build. The more I learn how these fields connect, the more meaningful the work becomes.
I already have some foundation in calculus and linear algebra, and I am continuing to build on it step by step.
| Area | What I am exploring |
|---|---|
| Mathematics | Nonlinear differential equations, calculus, and linear algebra |
| Physics | Developing better intuition for how real-world systems behave |
| Computer Science | Project-driven learning, especially in AI and machine learning |
I am particularly interested in nonlinear systems because the real world is almost never linear. If you want to understand complicated behavior in a serious way, you eventually have to deal with that complexity directly.
My journey into programming started a few years ago with Python. Over time, small experiments turned into larger pieces of code, and those pieces gradually turned into real projects.
The same pattern applies to mathematics and physics. The more I want to build, the more theory I need, so I keep returning to the fundamentals and studying them more deeply.
These are languages I have spent real hands-on time with, not languages I claim to have mastered. Python is currently my strongest language, especially for AI and data-related work. JavaScript and TypeScript have also been especially useful for web projects, while the others are languages I have explored to better understand different ways of thinking and building.
I do not see programming languages as trophies. They are tools. Different tools fit different problems, and I care more about understanding core concepts well than pretending to master everything at once.
I believe real understanding comes from trying to build something, making mistakes, and debugging through them. Reading matters, but it becomes much more valuable when I can apply an idea in practice.
I also prefer depth over surface familiarity. When I started learning machine learning, I did not just want to know how to use libraries. I wanted to understand what was happening underneath, which naturally pushed me back toward the math.
I also tend to work seriously. If I care about something, I prefer to give it my full attention instead of doing it halfway.
At the same time, I try to stay open to connections between fields. Sometimes physics sharpens intuition for a mathematical idea, and sometimes math gives a better way to think about an AI problem.
I am still early in the journey, and that is part of what makes it exciting. There is always more to learn, more to question, and more to build.
If you are interested in mathematics, physics, computer science, or AI, feel free to connect with me here on GitHub.