Dr. J. (Jazmin) Zatarain Salazar
I am an Assistant Professor at the Faculty of Technology Policy and Management in the Policy Analysis section. Previously, I was a postdoctoral researcher at the Environmental Intelligence for Global Change group at Polytechnic University of Milan, Italy. I hold a PhD in Civil and Environmental Engineering from Cornell University, U.S.A., with an emphasis in Environmental and Water Resources Systems Engineering where I was a researcher at the Decision Analytics for Complex Systems lab.
My research interests lie in advancing multiobjective decision support for environmental and water systems under climate and socio-economic uncertainty. This requires bridging hydrologic and climate sciences, artificial intelligence, high performance computing, economics, control theory, visual analytics and participatory policy design to capture the system’s complexity, to discover the tradeoffs across competing goals and to effectively guide discussions among stakeholders.
A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control
J Zatarain Salazar, PM Reed, JD Herman, M Giuliani, A Castelletti
Advances in water resources 92, 172-185
Balancing exploration, uncertainty and computational demands in many objective reservoir optimization
J Zatarain Salazar, PM Reed, JD Quinn, M Giuliani, A Castelletti
Advances in water resources 109, 196-210
Hippo Lab: Hyper-heuristics for Interpretable Public Policy Analysis
Highly consequential decision contexts such as climate change mitigation involve conflicts across multiple sectors, regions, and generations. Artificial Intelligence (AI) offers exciting opportunities for meaningful decision support in such contexts. However, to attain real-world impact, AI techniques must address the well-being of all stakeholders, minimize conflict, and protect vulnerable communities.
The Hippo lab aims to develop nature-inspired hyper-heuristic optimization methods, which capture the complexity of real-world problems in a flexible manner, and deliver solutions in a timely fashion for decision support. These methods are inspired in natural processes such as evolution, neural networks, ant colony behavior, and the motion of bird flocks.
The Hippo lab expects to use the tools it develops to achieve fair efficiency in public policy design and analysis. To assure broad societal benefits, first, we will advance AI on fair design by incorporating the principles of distributive justice to guide the exploration of policy alternatives as opposed to searching solely for efficient alternatives. Second, we will advance accountable design by improving interpretability of black-box optimization methods that enable great flexibility and efficiency at the expense of explainability, where we only observe the outputs related to the inputs of a given system but have no understanding of the inner workings and consequently about how a certain decision was reached. Finally, we will advance human-centered design by engaging stakeholders in systematic, AI-supported, deliberation and negotiation throughout the policy development cycle.