About Me
Applying data science to uncover meaningful insights in complex biological data.
I’m a data-oriented researcher with postdoctoral experience working with high-dimensional time-series data, including EEG and MEG. I began my work in MATLAB; however, I now primarily use Python for preprocessing, analysis, statistical modeling, and signal processing. My work focuses on turning complex, noisy data into clear and actionable insights.
My research background has shaped a structured, hypothesis-driven approach to problem solving. As a result, I work effectively with messy, real-world data. In addition, I prioritize clarity, reproducibility, and rigor in everything I build, from data pipelines to analytical models.
I approach problems systematically and break them into smaller, testable components. At the same time, I communicate results clearly and accessibly across both technical and non-technical audiences.
Currently, I am building practical data science projects and exploring applications in health, fitness, and medicine. Through this work, I continue to strengthen my skills in applied data analysis and real-world problem solving. This site showcases my projects, technical tutorials, and reflections on productivity and analytical thinking.
Outside of data science, I enjoy reading Japanese novels and playing soccer.
If you’re working with brain signal data or building tools in this space, I’d be glad to connect.
