I am an M.Sc. Data Science student with a background in Economics, interested in applying statistical, machine learning, and computational methods to scientific and geospatial data.

My current interests are increasingly focused on AI/ML, Earth observation, remote sensing, geospatial analytics, and scientific data analysis. I enjoy working with real-world datasets and building end-to-end analytical workflows, from data preprocessing and feature extraction to modelling, interpretation, and visualization.

My project experience includes working with satellite-derived datasets, environmental data, astronomical datasets, and large historical datasets using Python, R, SQL, PySpark, and Google Earth Engine. I have worked with datasets from sources such as VIIRS and MODIS, and have been developing further practical experience with satellite imagery and geospatial analysis.

I am particularly interested in exploring how machine learning and data-driven methods can contribute to scientific discovery, including applications involving space science and Earth observation. My participation in the NASA Open Science Data Repository Analysis Working Group (AI/ML) is an opportunity for me to learn from and contribute to collaborative scientific work involving AI/ML and space-related datasets.

I am still developing my specialization and actively learning through projects, research-oriented collaborations, and technical experimentation. I value reproducible analysis, understanding the limitations of models and data, and gradually turning exploratory work into rigorous, useful scientific outputs.