Research

Research by Kun Bu in statistics, Bayesian modeling, Financial NeQTL, human-centered AI, pharmacovigilance, and data science education.

My research focuses on developing statistical and data science methods for complex, high-dimensional, and interdisciplinary problems.

Research Areas

Bayesian Modeling and Structured Inference

I am interested in Bayesian methods, structured dependence modeling, and interpretable statistical learning.

Financial NeQTL / FiNeQTL

My current research develops genomics-inspired Bayesian frameworks for financial market prediction. This work adapts ideas from eQTL neighborhood structure to model relationships among sectors, industries, stock prices, and trading volume.

Human-Centered AI

I am interested in responsible and human-centered AI systems that support decision-making, implementation, teaching, and research.

Pharmacovigilance and Healthcare Data Science

My prior work includes medication safety, adverse event detection, drug-drug interaction analysis, and pharmacovigilance studies using large-scale healthcare databases.

Statistics and Data Science Education

I also work on improving statistics and data science education through applied examples, computational tools, and accessible teaching materials.