Carnegie Mellon University

Scott Matthews

Scott Matthews (E 1999)

Professor, Civil and Environmental Engineering and Engineering and Public Policy

Address
5000 Forbes Avenue
Civil and Environmental Engineering
123A Porter Hall
Pittsburgh, PA 15213

Bio

Scott Matthews is a professor in the Department of Civil and Environmental Engineering and the Department of Engineering and Public Policy at Carnegie Mellon University. He is also a member of the Green Design Institute, an interdisciplinary research consortium at Carnegie Mellon focused on modeling energy and environmental problems as systems, building decision support tools, and supporting robust policy decisions under uncertainty. Matthews has previously contributed to the development of tools for environmental and energy life cycle assessment (LCA) of products and processes (such as the EIO-LCA model), estimating and tracking environmental effects across global supply chains (such as carbon footprinting), and the sustainability of infrastructure systems. Matthews has served as chair of the Committee on Sustainable Systems and Technology with the Institute of Electrical and Electronic Engineers and on the Executive Committee for the American Center for Life Cycle Assessment. He participated in the National Research Council study on the Hidden Costs of Energy and is a member of the NRC Board on Environmental Studies and Toxicology.

Education

PhD 1999 - Carnegie Mellon University

MS 1996 - Carnegie Mellon University

BS 1992 - Carnegie Mellon University

Research

Matthews' research focuses on sustainable life cycle management of infrastructure, including transportation and building facilities, as well as energy, utility, and telecommunications networks.

Research Group: AIS 
Research Center: SiiCERCAMobility Data Analytics Center (MAC)

Projects

Matthews’s research and teaching focus on valuing the socioeconomic implications of social systems, such as energy and transportation infrastructure. His work intends to facilitate economic and social decision-making under uncertainty via large datasets, computation, and visualization methods. 

Publications

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