Thank you for contacting the SEED Research Group. Let us help you engage your interests in environmental and infrastructure decision making! Here is a short description of our research vision, also reproduced on our “Research Vision” page. Feel free to contact by email or phone for more details!
Our overall research vision is SEED—Planted, where SEED means “Sustainable [Urban] Ecologies, Engineering, and Decision-making.” Our research interests include:
- Infrastructure management, including sustainability assessment and risk analysis;
- Urban sustainability definition and decision making;
- Regulatory risk assessment and policy-focused research, especially for environmental contaminants and infrastructure systems; and,
- Statistical/mathematical modeling approaches to decision support.
As you may know, it is difficult to unify these themes under one umbrella. We tend to think of ourselves as operating under the Earth Systems Engineering and Management (ESEM) paradigm for civil/environmental systems design[1]. ESEM is an approach to engineering research and education that seeks to address the irreducible complexity of tightly coupled environmental, social, and technical systems in attendant design and analysis.
To organize my research mission into tractable parts under the ESEM vision, and due to the tight coupling of infrastructure systems engineering and policy, my research group’s activities will be organized according to the “policy-as-learning” paradigm[2]:
- Technical Learning
- Conceptual Learning
- Social Learning
To understand a little better what these types of policy learning mean, consider the following emerging problems in infrastructure management and policy-focused environmental modeling and analysis:
Technical Learning. Technical learning involves an evolution of engineering tools supporting decisions in the context of well-defined, static policy goals. In this area, my research group would focus primarily on innovation in the development and interpretation of statistical methods to enable real-time management of drinking water distribution systems. Recently, the National Academies has evaluated drinking water distribution system research and policy needs. Specifically, the NRC suggests “distribution system integrity is best evaluated using on-line, real-time methods,” but that “research is needed to better understand how to analyze data from online, real-time monitors in a distribution system.” To address this problem, I plan to employ data mining techniques and probabilistic graphical models to support the development of “intelligent” and adaptive distribution system management, focusing on distribution system rehabilitation planning. For example, Bayesian Belief Networks may be used in conjunction with supervisory control and data acquisition methods to not only predict unintentional contaminant intrusion events (e.g., pipe breaks), but also minimize population exposure to such contaminants in real-time. The objective of this research is to develop distribution system design and retrofitting techniques that facilitate real-time risk management.
Conceptual Learning. Conceptual learning involves the evolution of engineering tools supporting decisions in the context of changing policy goals. Conceptual learning also involves the definition of new concepts representing the challenges of unsolved problems for which technical learning is inadequate. The concept of “sustainability” is hotly debated, and provides a rich conceptual learning context for my research interests in infrastructure management. In this area, my research group would focus primarily on development of decision analysis tools for evaluating the sustainability of urban infrastructure systems. Consider for a moment the sustainability of drinking water systems. As a postdoctoral fellow, I am currently working with Dr. Seth Guikema to identify infrastructure performance metrics for drinking water networks. As a new faculty member my research group would start with a focus on developing software that will use evolutionary optimization algorithms to facilitate integration of financial goals, technical requirements, and ecological constraints in the design of drinking water treatment plants and distribution systems using the metrics Dr. Guikema and I will have developed. These tools would then be extended to other urban infrastructure systems, including buildings, electricity and energy, and transportation.
Social Learning. Social learning involves the evolution of engineering tools supporting decisions in the context of not only changing policy goals, but also changing social preferences, perspectives, and capabilities. Consequently, social learning requires that relationships between stakeholders be explicitly considered as critical components of technical and policy solutions. In this area, my group will explore the relationships among urban infrastructure network topology, urban ecological space[3], and vulnerability to natural or man-made hazards. My research group will employ statistical learning methods, decision analysis tools, economic input-output life cycle analysis, and agent-based modeling techniques to answer the question to answer the question “How will perceptions of global environmental problems change the sustainability of cities, especially as cybernetic[4] (e.g., smart grid) infrastructures are developed?”
[1] Allenby, B. (2005) “Earth Systems Engineering and Management.” Environmental Science and Technology…[2] Fiorino, D. (2005) The New Environmental Policy. MIT Press, Cambridge MA.
[3] Alberti, M. (1996) “Measuring urban sustainability.”
[4] de Rosnay, J. (2000). The Symbiotic Man: A New Understanding of the Organization of Life and a Vision of the Future. McGraw Hill.