About Me

My curiosity for computers started early. I was always fascinated by how software worked, how websites responded to a click, and how a few lines of code could turn into something real on screen. That curiosity slowly turned into a habit of experimenting, breaking things, and figuring out how to fix them.

I took that interest seriously when I joined MLR Institute of Technology for my B.Tech in Computer Science (Data Science). Once I started writing code properly, I loved the feeling of building something from scratch and watching it work. Over time, it stopped being only about how things looked. I started caring more about what was happening behind the scenes: how data moves, how APIs are structured, why something becomes slow, and how systems can be made faster, cleaner, and more reliable.

During this phase, I built dozens of projects. Most of them broke. A few actually worked, like my RFP Management Automation System, which uses retrieval-augmented generation to analyze proposal documents, and a Deepfake Detection System built with CNN-based deep learning. Every bug, failed build, and late-night debugging session helped me understand software a little better.

Along the way, I also tested myself outside the classroom. Winning the Zignasa National Level Hackathon and finishing as runner-up at a Project Expo taught me how to build under pressure, communicate ideas clearly, and turn vague problems into concrete solutions.

That curiosity never really went away. I still find myself experimenting late at night, reading about new technologies, breaking things to understand them better, and thinking about what to build next. There is always something new to learn, something better to create, and something I do not fully understand yet. That is what keeps me going.