I am a research assistant and MSc Public Health Nutrition candidate with experience translating scientific evidence into clear, accessible writing for academic and general audiences.
I combine research, technical writing, evidence synthesis, and machine learning.
I have co-authored approximately eight peer-reviewed journal articles across food science, agriculture, and biochemistry. I also write The Intuitive Pythonista, a storytelling-based newsletter that introduces machine learning concepts to beginners.
My machine learning experience spans data cleaning, feature engineering, classification, clustering, time-series forecasting, object detection, model validation, and ensemble modeling. I have delivered end-to-end analyses from raw data through modeling and published technical write-ups.
I enjoy applying data science to practical problems with evidence in content optimization, climate and crop advisory, financial inclusion, food detection, healthcare, and nutrition. I am especially interested in roles where research rigor, Python, machine learning, and clear communication intersect.
I am currently completing an MSc thesis on the effectiveness of a mobile-based nutrition intervention on dietary diversity among women of reproductive age.
