Michael Chrzan’s research interests lie at the intersection of education, data science, and equity. He is passionate about leveraging machine learning, causal inference, and natural language processing to solve urgent challenges in K–12 education. His work focuses on the ethical and effective application of AI to promote educational equity, particularly in areas such as scenario modeling for school closures, predictive analytics, and developing tools for use by schools and policymakers. Michael’s projects include analyzing large-scale educational datasets, developing algorithms for decision support, and using quantitative and qualitative methods to unpack the impacts of policy and practice on student outcomes. He is also drawn to exploring how community feedback, parental beliefs, and systemic inequities inform educational reform, striving to ensure that data-driven innovations are driven by those they most effect.
