Sergey Kushnarev is a senior lecturer in the Department of Applied Mathematics and Statistics at the Johns Hopkins Whiting School of Engineering. He is the 2026-2027 William R. Kenan, Jr. Scholar/Teacher, recognized for his project, “AI-Supported Formative Assessment for Improved, Self-Regulated Learning in Engineering Statistics Courses,” which explores how real-time AI-driven feedback can support student learning in engineering statistics.
Kushnarev’s teaching and research focus on applied statistics, Bayesian methods, time series analysis, probability, and computational mathematics, with an emphasis on making statistical reasoning accessible to students with diverse academic backgrounds. He develops courses that incorporate inclusive design principles, structured applied projects, and multiple pathways for demonstrating mastery. His work on personalized online homework systems in large-enrollment calculus courses has demonstrated measurable learning gains, while his research on virtual reality–based visualization tools has shown improved student understanding of partial derivatives in multivariate calculus.
He developed a first-year seminar on causal inference to introduce students early to the core questions of statistical reasoning and serves as a faculty mentor for the Freshman Experience in Applied Mathematics and Statistics program. He has received multiple recognitions for teaching excellence, including the William H. Huggins Excellence in Teaching Award and two Professor Joel Dean Awards for Excellence in Teaching.
Prior to joining Johns Hopkins, Kushnarev served as a lecturer and senior lecturer at the Singapore University of Technology and Design from 2013 to 2021, where he led courses in probability, statistics, optimization, linear algebra, and multivariate calculus and co-chaired the Undergraduate, Accreditation, and Pedagogy Committee.
His scholarly work lies at the intersection of computational geometry, statistical methodology, and applied data analysis, with applications spanning medical imaging, astrophysics, and gravitational wave science. He received his PhD and master’s degrees in applied mathematics from Brown University and holds a red diploma in mechanics and applied mathematics from Moscow State University.