Chuan recently received her Ph.D’s degree in public health. Her main research areas are environmental health sciences, with a focus on environmental epidemiology. By using a series of data collection, statistical analysis, and modeling, her research studies the relationship between prenatal methylmercury exposure and neonatal outcomes. For her capstone project at NYC Data Science Academy, she built a complete machine learning pipeline to track and predict food desert in US. This pipeline consists of web scraping data from various sources, collecting real-time Twitter data through its Streaming API, natural language analysis (e.g. LDA topic modeling), and classification modeling, such as logistic regression, random forest, and XGBoost etc.
