I build Python backend systems and reproducible data workflows, with a focus on financial time series, statistical modeling, and machine learning.
- 🐍 Building backend applications with Python, Flask, SQLAlchemy, and relational databases
- 📊 Working on financial time-series analysis, covariance estimation, and statistical modeling
- 🧠 Designing reproducible machine-learning experiments with PyTorch and diffusion models
- 🐳 Packaging and testing applications with Docker, Alembic, and pytest
- 🌏 Based in Japan and open to backend, data analytics, and collaborative opportunities
A reproducible Python research codebase for sliding-window covariance estimation using conditional diffusion in time and Fourier domains. It evaluates financial time-series datasets against classical statistical baselines through data preparation, training, metrics, and visualization workflows.
Stack: Python · PyTorch · pandas · Time Series · Statistical Modeling · Jupyter
A role-based equipment-inspection web application for field engineers and administrators, featuring authentication and authorization, equipment/account/inspection CRUD, transactional services, database migrations, Docker deployment, and HTTP integration tests.
Stack: Python · Flask · SQLAlchemy · MySQL / TiDB · Alembic · Docker · pytest
- AWS Certified Cloud Practitioner — Amazon Web Services Training and Certification
- Japan Statistical Society Certificate (JSSC), Grade 2
- Data Science Education Program Level 4 — Business — Tokyo University of Science