Bridging the gap between
Research & Engineering.

I am a software engineer with a strong focus on backend systems, applied machine learning, and data-driven problem solving. My work sits at the intersection of software engineering and research, where I design systems that prioritize reliability, scalability, and measurable impact.

I have experience building end-to-end machine learning pipelines, backend services, and research-oriented projects that emphasize correctness, performance, and reproducibility. My projects often involve working with real-world datasets, implementing evaluation metrics, and translating experimental results into practical system designs.

Alongside development, I am deeply interested in how large software systems fail, how they can be tested more effectively, and how data and machine learning can be used to improve software reliability. This interest has guided my independent research work in areas such as bug prediction, benchmarking, and applied ML for decision-making systems.

I aim to pursue advanced study in software engineering and related fields, with long-term interests in scalable systems, testing, and applied AI. I value clarity over complexity, systems over scripts, and long-term engineering principles over short-term trends.