Professor Johnson received his Interdisciplinary Ph.D. in Economics and Mathematics from the University of Missouri-Kansas City. Prior to joining McIntire, Professor Johnson taught courses in economics, statistics, and analytics at Maryville College in Tennessee and at Loyola University Chicago's Quinlan School of Business. He teaches quantitative analysis, customer analytics, and marketing research, with an emphasis on making statistical and machine learning tools accessible to students at every level of technical background.

Professor Johnson's current research is in Bayesian machine learning and specifically reinforcement learning applications. As a research fellow at Stacker AI Lab, he works on AI systems built on probabilistic methods. His methodological interests include Bayesian statistical inference, probabilistic machine learning multilevel modeling and probabilistic graphical models. 

His academic research examines business-cycle theory, financialization of U.S. economy, and the history of financial crises. This work has appeared in the Journal of Post Keynesian Economics; The European Journal of the History of Economic Thought; and the Handbook of Research Methods and Applications in Heterodox Economics.