معرفی
Marc Scott is a Professor of Applied Statistics and Co-Department Chair at the Steinhardt School of Culture, Education, and Human Development at New York University (NYU). He also serves as Co-Director of the PRIISM Center, focusing on methodological innovation at the intersection of information and social science. His research emphasizes statistical modeling of longitudinal data, particularly in labor markets, health trajectories, and educational pathways. Dr. Scott co-founded the MS-A3SR program, a master’s degree in Applied Statistics for Social Research, and has led the development of advanced computational methods for policy analysis.
His research interests include Bayesian models, causal inference, multilevel modeling, and categorical sequence analysis. Notable contributions include work on career mobility patterns, wage inequality trends, and the statistical analysis of life histories. He teaches courses such as Multi-Level Models, Spatial Statistics, and Machine Learning, integrating cutting-edge methodologies with practical applications in social science research.
Dr. Scott’s work bridges academic and applied realms, with projects addressing low-wage labor markets, maternal mental health during crises, and obesity prevention in vulnerable populations. His methodological advancements, such as model-based clustering and sensitivity analysis for multilevel models, have been widely adopted in policy-relevant research.
He has held leadership roles in NYU Steinhardt’s research infrastructure, including co-directing the Interdepartmental Research Studies (IDRS) program and spearheading the PRIISM Center. His collaborations span institutions and disciplines, emphasizing rigorous yet accessible statistical tools for addressing societal challenges.
