The End of Fall Semester is approaching.

As I return from a three-day break in Toronto, the winter chill reminds me that my first semester is coming to a close. Looking back, the last few months have been a blur of statistical formulas and R code. It wasn’t just about adapting to a new city; it was about shifting my mindset from simply running code to understanding the mathematical heartbeat behind it.

For me, the statistics and data science courses felt quite fresh; I was introduced to a lot of math, models, and formulas. I realized that you can’t just use R to do linear regression; you need to understand the math behind it. Consequently, in Fall 2025, I immersed myself in statistical theory and data science fundamentals, such as linear regression, data processing, and data visualization.

Beyond my coursework, I continued driving development on VizThinker. I focused on optimizing the UX/UI and engineered a new feature that allows users to upload and process external files and images, significantly expanding the platform’s utility.

Simultaneously, I contributed to the Lilac project, where I tackled the challenge of multi-cloud observability. My role involved architecting a unified pipeline to discover and aggregate resource states across AWS, Azure, and GCP. Navigating the complexities of these distinct cloud provider APIs deepened my technical command of cloud infrastructure and cross-cloud interoperability.

If Fall 2025 was about building the theoretical foundation, Winter 2026 will be about getting my hands dirty. I am eager to take the equations off the page and into the real world with Machine Learning and Embedded Systems.