Computer Science • Data & ML • Open-Source Builder
Code with purpose. Build systems that remove friction.
- Computer Science graduate with fundamentals-first thinking
- Build data-driven and utility-focused systems
- Focused on Python, Data Science, Machine Learning, and applied AI
- Care about clarity, reproducibility, and low-friction design
- Currently balancing academic depth (NET) with practical engineering
- Strengthening core CS, statistics, and ML foundations
- Building small, focused open-source utilities
- Academic + applied projects in data analysis and ML evaluation
- Long-term direction toward AI systems and research-grade work
- readme-first — CLI tool to evaluate whether a repository is actually runnable for first-time users
- PCOS Prediction Framework — Feature selection + multi-model ML comparison with clear metrics
- Academic Vault (Android) — Lightweight academic resource hub, optimized for clarity and access
- ML Experiments Lab — Structured experiments exploring models, assumptions, and trade-offs
I value projects that:
- Solve one clear problem
- Are easy to run and easy to understand
- Respect developer time and attention
- Treat learning as a system, not scattered notes
Open source is not about volume.
It’s about reducing friction for the next developer.
“Build things that still make sense six months later.”