About

I'm Zijian (Michael) Wang, a data scientist and aspiring AI researcher currently pursuing my M.S. in Statistics at Georgia Institute of Technology, with a B.S. in Quantitative Sciences (Data Science track) and a minor in Computer Science from Emory University.

My academic journey has been rooted in a passion for understanding data, extracting insights, and solving real-world problems using rigorous quantitative methods. I've been actively involved in research on causal inference under image confounding, developing novel approaches with Neural Tangent Kernel (NTK)-based inverse probability weighting. My honors thesis titled “Mini-Max-Structured Neural Tangent Kernel in Estimating Average Treatment Effect Confounded by Image Covariate” combines statistical theory with machine learning techniques, and has been recognized for its innovation.

I'm equally enthusiastic about practical applications. I've led and collaborated on several projects in sports analytics, natural language processing, and environmental justice. These include evaluating player strategies for the Atlanta Braves, analyzing misogyny in rap lyrics using NLP, and forecasting environmental changes in underserved communities using GIS and time-series modeling. For more, visit my portfolio.

My technical toolkit includes Python, R, Java, SQL, and C, and I frequently work with libraries and tools such as PyTorch, scikit-learn, FastAPI, and Neo4j. I’m currently integrating AI-native automation into construction site reporting using DeepSeek API, large language models, and knowledge graph RAG systems.

Outside of academics and research, I'm passionate about basketball (huge Golden State Warriors fan!), jazz saxophone, fitness, and exploring new hiking trails. I'm always open to collaborative opportunities at the intersection of statistics, machine learning, and impactful real-world use cases.