Research with Prof. Laurent (Harvard)
🧠 Integrated Learning: From Theory to Application
Under the thoughtful guidance of my professor, I not only mastered the theoretical foundations of projectile motion and the least squares method but also successfully bridged the gap from mathematical derivation to practical visualization using Jupyter Notebook.
In the study of projectile motion, we systematically explored trajectory equations for different types of motion, such as oblique and horizontal launches. By adjusting parameters like initial velocity, launch angle, and gravitational acceleration in Jupyter Notebook, I was able to dynamically visualize the parabolic paths, which deepened my understanding of how factors such as air resistance influence trajectories.
In the least squares fitting module, I learned to use Python's NumPy and Matplotlib libraries to perform curve fitting on experimental data. From linear regression to polynomial fitting, each round of code debugging and parameter tuning helped me clearly grasp how data errors impact model accuracy.
This theory-to-practice approach allowed me not only to gain a deeper understanding of fundamental physical laws but also to develop hands-on skills in data analysis. Moreover, these skills are highly transferable to future research projects. For instance, when analyzing large-scale behavioral data from ADHD and non-ADHD participants, techniques like least squares fitting can be used to extract features, analyze trends, and test for group differences—ultimately providing a robust quantitative foundation for scientific conclusions.
