Michael Leon Chrzan

Data Scientist bridging K12 education practice, policy, and advanced analytics through machine learning, causal inference, and NLP to drive educational equity and evidence-based improvement at scale.

I am a Data Scientist and former Master Teacher with over a decade of experience bridging education practice and data-informed decision-making. I currently work as a Data Scientist at the Center for Educational Data Science and Innovation (EDSI) after recently completing my M.S. in Education Data Science at Stanford University’s Education Data Science Master’s program. I specialize in machine learning, predictive and causal analytical methods, and natural language processing to tackle pressing challenges in K-12 education and beyond. My work and research thus far have focused on leveraging data science to inform equitable school closure decisions, improve systemic outcomes, and support schools, districts, and EdTech companies in using data to drive meaningful change.

My undergraduate training at the University of Michigan in mathematics and education, enabled by my Gates Millennium and Coca-Cola scholarships, sparked a commitment to becoming the best teacher I could be while advancing the profession of teaching. There I learned the importance of developing a research-based practice that pursues what works broadly for all children, rather than replicating what worked for me as a student—a tradition ripe with biases that can be reproduced across generations.

Combining rigorous coursework in mathematics and computer science with real-world teaching experience, I’ve built a career spanning highly effective teaching, transformative school leadership, and now data science research. As a teacher at Promise Schools, I led my Grade 10 team to historic PSAT gains (+100 points composite). As Math Master Teacher at Detroit Public Schools Community District, I mentored 20+ teaching interns, developed data protocols for our Instructional Leadership Team, and built an innovative Project Based Learning framework that resulted in measurable growth on teaching effectiveness metrics.

At Stanford, I specialized in causal methods, measurement, networks, and natural language processing, earning a Dean’s Fellowship and graduating with a 4.1 GPA. My current work at EDSI focuses on AI for equity in education, scenario modeling for school closure policies, and the ethical application of AI and machine learning. I analyze classroom recordings, administrative datasets, and AI-generated data to build actionable tools for schools and policymakers.

Experiencing firsthand how inadequate our current systems are at equipping teachers with resources to improve instruction and providing schools with insights to support vulnerable students drives my work. I’m passionate about moving from influencing thousands of students in classrooms to having a national and global impact through data-driven research and evidence-based policy.