Johann Gagnon-Bartsch is an Associate Professor in the Department of Statistics at the University of Michigan, Ann Arbor, and serves as Director of Undergraduate Programs and Associate Director for Undergraduate Programs. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Center for Computational Medicine and Bioinformatics. His research focuses on causal inference, machine learning, and nonparametric methods with applications in biological and social sciences. He holds a PhD in Statistics from UC Berkeley (2012), an MS in Statistics from UC Berkeley (2007), and a BS/BA in Math, Physics, and International Relations from Stanford University (2003). Education: PhD in Statistics, UC Berkeley, 2012 MS in Statistics, UC Berkeley, 2007 BS/BA in Math, Physics, and International Relations, Stanford University, 2003 Research Interests: His work spans high-throughput biological data analysis, particularly in addressing systematic errors via negative controls, and integrating experimental/observational data. Recent focuses include causal inference in education experiments, social media analytics for public opinion tracking, and reproducible research workflows. He develops methods to correct for unobserved confounders and laboratory variability in genomic datasets. Grants & Awards: MIDAS Reproducibility Challenge Winner (2020) Thomas R. Ten Have Award (2016) Evelyn Fix Prize (2013) Outstanding Graduate Student Instructor (2010) Teaching & Mentorship: Teaches courses in applied statistics and statistical learning. Mentors numerous PhD, master's, and undergraduate students in areas like genomic data analysis and causal inference. Active in curriculum development and student advising within the Statistics Department. Labs & Collaborations: Leads projects at MIDAS and collaborates with researchers in computational medicine, bioinformatics, and social data science. His lab develops open-source tools like the RUV package for batch effect correction and Docker-based reproducibility workflows.










