School of Humanities and Sciences
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Michael Baiocchi
Associate Professor of Epidemiology and Population Health and, by courtesy, of Statistics and of Medicine (Stanford Prevention Research Center)
BioProfessor Baiocchi is a PhD statistician in Stanford University's Epidemiology and Population Health Department. He thinks a lot about behavioral interventions and how to rigorously evaluate if and how they work. Methodologically, his work focuses on creating statistically rigorous methods for causal inference that are transparent and easy to critique. He designed -- and was the principle investigator for -- two large randomized studies of interventions to prevent sexual assault in the settlements of Nairobi, Kenya.
Professor Baiocchi is an interventional statistician (i.e., grounded in both the creation and evaluation of interventions). The unifying idea in his research is that he brings rigorous, quantitative approaches to bear upon messy, real-world questions to better people's lives. -
James Balamuta
Lecturer
BioJames is an adjunct lecturer in the Department of Statistics for the 2024 Autumn Quarter. He serves as the founder of HJJB, LLC, which offers specialized data science guidance and solutions to startups, fortune 500 companies, and academia across the U.S. He holds a Ph.D. in Informatics from the University of Illinois Urbana-Champaign (UIUC). Previously, he was a Visiting Assistant Professor in Statistics at UIUC where his research focused on latent variable estimation under restricted latent class models and computational statistics. For his work, he was awarded the 2022 Psychometric Society Dissertation Prize and was a co-recipient of the 2021 Bradley Hanson Award for Contributions to Educational Measurement. During his graduate studies, he contributed significantly to Department of Statistics’ education initiatives in data science and earned accolades, including the Department of Statistics Doctoral Student Teaching Award in 2019. His multifaceted career reflects a commitment to advancing research, education, and practical applications in data science.
