I spent many years developing risk-based tools to support environmental decision-making. That has led to an interest in tools and methods to support sustainable approaches to making environmental decisions, particularly in terms of economic benefits related to ecosystem services. A key interest is the development of methods for quantifying changes in ecosystem services and relating those to changes in benefits using stated preference methods. I am interested in methods that integrate economics and risk assessment to better quantify the benefits of proposed risk reductions as a result of management or regulatory actions for use in cost-benefit, cost-effectiveness, and value of information analyses. Much of my work has focused on incorporating quantitative uncertainty analysis (e.g., analytical, probabilistic,and fuzzy methods) into the risk assessment process, and I have been at the forefront of the effort to promote uncertainty analysis and methods for communicating uncertainty to support environmental decision-making.
Examples of Projects
- Currently Principal Investigator for a project to develop better methods for incorporating uncertainty analysis results from complex, integrated models into the decision-making process using a case study of exposures to mercury.
- Explored the use of stated preference methods for eliciting monetized values consistent with economic theory for risk reductions associated with exposure to PCBs in fish tissue. Developed valuation functions for the developmental health and ecological risk reductions, and incorporated them into a risk assessment model using the Hudson River as a case study.
- Led the effort to develop a probabilistic decision-making framework for evaluating the suitability of disposal of dredged materials at the Historic Area Remediation Site (HARS) in NY-NJ Harbor.
- Developed a screening-level tool to quantify the risk-risk and risk-social cost tradeoffs associated with different scenarios of future technology implementation in the electricity sector.
- Technical lead for the development of a probabilistic bioaccumulation model used to evaluate remedial alternatives for the Hudson River Superfund Site, and for the ecological risk assessment, which incorporated a joint probability model for predicting potential effects.
- Led the effort to develop a prototype Bayesian hierarchical model for predicting the potential for ecological effects associated with exposures to military unique compounds (e.g., smokes and obscurants, energetics) for which toxicity is poorly characterized and/or highly uncertain.
AB, Harvard College, cum laude
ScM, Harvard School of Public Health, Environmental Health and Health Policy and Management
ScD, Harvard School of Public Health, Environmental Science and Risk Management