1 

Python for Machine Learning & Deep Learning

I have experience using Python for both machine learning (ML) and deep learning (DL) applications. In deep learning, I primarily use PyTorch to build, train, and optimize neural networks for data analysis and pattern recognition. Additionally, in machine learning, I utilize Scikit-learn to implement various algorithms, including regression (Linear Regression, Ridge, Lasso), classification (SVM, Random Forest, KNN), dimensionality reduction (PCA), and clustering (K-Means). These skills enable me to analyze complex research data and select optimal models to quantitatively evaluate experimental results.

2

MATLAB Simulink

I have hands-on experience using Simulink to simulate and optimize the gear ratio of a gearbox by analyzing the relationship between motor output, battery pack performance, and track profiles. In this project, I modeled the system’s dynamic behavior, performed parameter optimization, and analyzed how different gear ratios impact vehicle energy efficiency. Through this experience, I have developed strong proficiency in Simulink’s block-based modeling approach, allowing me to accurately simulate and analyze complex mechanical systems. Additionally, I am capable of implementing parameter sweeps and extracting meaningful insights to support mechanical system optimization.

3

C/C++ for Embedded Systems

I develop embedded firmware in C, enabling real-time control and data acquisition from sensors and actuators. Additionally, I am proficient in Linux-based embedded systems, handling system-level programming and integrating hardware with software for reliable and efficient operation in research applications.