Machine Learning Radar Research

My sophomore year internship was a major growth period for me. I received invaluable mentorship from talented PhD-acclaimed researchers. From my first day in the office, I was tasked with learning as much as possible about computational electromagnetics simulation and radar cross-section analysis. After my initial orientation to department-specific technology, I began running simulations on various geometries.

One of my biggest challenges was learning a proprietary CAD tool unlike anything I had worked with before. At first, it felt clunky and rigid, but over time, I grew to appreciate its power. It provided robust tools for mass scripting, mesh generation, and refinement, making the simulation process far more seamless than I initially expected.

Beyond simulations, I gained hands-on experience with high-performance computing and cutting-edge machine learning applications in both private and public industry. Formatting and cleaning simulation data with Pandas and NumPy, then preparing it for training with tools like PyTorch and Scikit-learn, gave me a practical understanding of what it takes to turn raw data into a reliable, actionable model. This experience deepened my appreciation for the complexity behind building machine learning systems that deliver real-world results.