I’m interested in solving real-world problems through data-driven systems, research and practical design. My work focuses on using machine learning and scientific computing to build tools, systems, and insights that are both functional and meaningful.
Languages
Python • JavaScript
Machine Learning & Data Science
NumPy • Pandas • Matplotlib • Seaborn • Plotly • Scikit-Learn • TensorFlow • PyTorch
Web Development
Node.js • Vue • TailwindCSS • SQL • Flask • FastAPI
Other Tools
Git • VS Code
I’m always exploring new libraries, frameworks, and ways to improve my workflow and expand my technical toolkit.
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Applied Machine Learning
Designing ML systems that tackle real-world challenges, with a focus on practical deployment and reliability. -
Model Interpretability
Building models that not only perform well but can be trusted, understood, and explained—especially in high-stakes domains like healthcare or finance. -
Scientific & Research Computing
Leveraging computation to explore and answer complex questions, particularly in interdisciplinary research settings.