We challenge each other to grow and push the boundaries of science, while using transparent expectations and evidence-based best practices to create a classroom and lab environment that is welcoming to all.
We set clear expectations for all lab members (including the PI!) and practice clear communication to ensure a welcoming and productive work environment. Check out our lab policies, co-developed by lab members.
We welcome lab members from all backgrounds and set clear policies to create a safe and welcoming lab for everyone. We take action to address systematic inequities in our scientific institutions.
Addressing multidisciplinary water challenges requires diverse skills and perspectives. Our lab members have wide-ranging expertise including systems modeling, data science, hydrology, and policy analysis.
Water resources planners use computational systems engineering models to inform decisions about operations, infrastructure development, and policy. Systems models evaluate alternative decisions against performance metrics like water reliability, access, cost, electricity production, and ecosystem services under a range of hydrological and social conditions. This course will introduce computational methods used in decision-support and common applications in water resources. Attention will be given to multi-objective analysis and climate change adaptation.
Hydrological processes like precipitation, streamflow, and groundwater flow are highly variable over time and across locations. Quantifying the uncertainty in hydrological models and simulating future conditions is critical for informing the development and management of civil infrastructure systems. This course introduces students to statistical methods used in hydrology for data analysis, risk and uncertainty analysis, and simulation. Topics include: flood and drought frequency, time series analysis, and synthetic streamflow generation.
Policymakers rely on quantitative systems models to inform decision-making about environmental policy design, infrastructure development, and resource allocation. However, many rapid, transformational changes in the climate and socioeconomic systems are difficult to predict and quantify in models. Therefore, reliance on traditional model-based decision analysis can leave policymakers vulnerable to unforeseen risks. In this class, students will learn quantitative methods for addressing “deep” uncertainties using systems modeling, enabling them to identify potential vulnerabilities and design decision policies that are robust and resilient to a wide range of uncertain futures.
We are building a diverse lab that is welcoming for all. We are specifically looking for students who are passionate about using engineering tools and policy analysis to create a more sustainable and just world.
