My objective is to contribute to research-oriented or applied machine learning teams, where mathematical rigor, model understanding, and careful evaluation matter.

I am particularly interested in roles involving:

• Machine learning and statistical modeling
• Model evaluation, optimization, and interpretability
• MLOps practices for reproducible and maintainable ML systems

I am currently seeking a 6-month internship in Machine Learning or Data Science in 2026, with a flexible start date.

Beyond my core academic and technical projects, I actively explore modern AI systems, including LLMs, RAG, and practical MLOps patterns for building reproducible ML workflows.

I value continuous learning through personal projects, technical reading, and participation in hackathons or collaborative initiatives, with a strong emphasis on clarity, reproducibility, and engineering discipline.

In the medium term, I plan to:

• Participate in Kaggle competitions to strengthen my applied ML skills and benchmarking mindset
• Share learnings through short technical write-ups (e.g., Medium / Substack) and community initiatives (e.g., DeepLearning.AI)
• Contribute to open-source projects when relevant, especially around data pipelines and ML tooling

Outside of tech, I also enjoy guitar and singing, which helps me stay creative and consistent 🎸