Mohsen Shahmohammad
Mohsen is an interdisciplinary researcher investigating climate-resilient urban solutions through green infrastructure and cutting-edge technologies like blockchain to advance sustainable development goals. His current work focuses on optimizing diverse green infrastructure systems, such as green roofs, to enhance urban ecosystems. Through extensive research, he has developed design strategies that boost the ecological benefits of green roofs while improving building energy efficiency, contributing to more sustainable and livable cities. Prior to joining SEAS, Mohsen was a Research Assistant at Michigan State University, where he participated in the Sustainable Agriculture and Food Systems (SAFS) graduate specialization program. Recently, he has expanded his research to include the study of constructed wetlands for stormwater management and pollutant removal, with a particular focus on performance variations in cold climate regions. His research employs a range of analytical tools and modeling approaches, including SWAT (Soil and Water Assessment Tool) for hydrological modeling and DesignBuilder for building performance simulation, among others.
Specialization program, Sustainable Agriculture and Food Systems (SAFS), Michigan State University, 2024
M.Sc., Environmental Engineering, Iran University of Science and Technology, 2022
Shahmohammad, M., Salamattalab, M. M., Sohn, W., Kouhizadeh, M., & Aghamohmmadi, N. (2024). Opportunities and obstacles of blockchain use in pursuit of sustainable development goal 11: A systematic scoping review. Sustainable Cities and Society, 112, 105620.
Naderian, D., Noori, R., Bateni, S. M., Jun, C., Kim, D., Shahmohammad, M., ... & Woolway, R. I. (2025). Pivotal role of snow depth, local atmospheric conditions, and large-scale climate signals on ice thinning in Finnish lakes. Science of the Total Environment, 966, 178715.
Mahdian, M., Noori, R., Saravani, M.J., Shahvaran, A.R., Shahmohammad, M., Gaffney, P.P., Salamattalab, M.M., Anboohi, M.S., Hosseinzadeh, M., Xia, F. and Zhou, Y., 2026. Linking Hypolimnion to Epilimnion in a Stratified Arctic Lake: Machine Learning-Based Estimation of Hypolimnetic Water Quality from Epilimnetic Measurements. Water Research, p.125367.