My doctoral work will evaluate opportunities to replace traditional lawns by more sustainable and ecologically friendly crops in the Greater Chicago Metropolitan area. Specifically, I will  pursue three broad objectives: (1) Use aerial images to identify lawns in Chicago and Evanston and quantify the potential for lawn replacements in the city. (2) Use ethnographic work to assess homeowners’ incentives to replace their lawns with other crops. (3) Conduct an experiment to test the water filtration/retention capacity of different lawn replacements. Support from the IISG Graduate Scholars program would enable me to pursue the second objective. Many research projects related to urban ecology use census and survey data to understand the social aspects of restoration within urban communities. The ethnographic interviewing method involves participant observation which allows for a more determined selection of interviewees as well as a more in-depth line of questioning than the survey data might provide. I will also conduct participant observation for this project which will involve interacting directly with interviewees while they maintain, cultivate, and enjoy their own green spaces.

During the given one-year research period, I plan to develop a data-driven model integrating the physical model of infrastructure vulnerable to hazard and artificial intelligence machine learning algorithms to offer precautions and suggestions to resist natural hazards and enhance infrastructure flood resilience for the southern Lake Michigan communities. The proposed research targets to provide coastal communities with on-time and accessible suggestions to resist flooding attacks, support coastal industrial development without interference, give organizations reasonable, efficient recommendations to minimize the flooding impact on infrastructure, and offer the government customized design advice for infrastructure in the southern Lake Michigan region. Most importantly, this research will call public attention to the resilience of coastal communities and infrastructure.

This research project proposes a computational model validated by experimental testing to improve various characteristics of sustainable cement and concrete by reusing waste materials from the Great Lakes as additives in sustainable building materials. The model will be paired with AI algorithms to efficiently determine the feasibility of recycling bottom sediment from the Great Lakes as a sustainable construction material and analyze the impact of the additive on concrete performance. The goal of the proposed research is to reduce environmental pollution and improve the current ecological system by recycling the bottom sediments in the Great Lakes region, thus improving the efficiency of concrete use in actual construction and the ecological sustainability of the Great Lakes region.

In this one-year research project, I propose a data-driven fragility curve to investigate the damage level of the levee system considering the structural conditions, flooding level and environmental conditions in the Southern Lake Michigan region and provide recommendations on the maintenance strategy suitable for the flooding area. The goal of the proposed research is to mitigate potential losses, improve the current levee system maintenance and therefore enhance the flood resilience of the levee system and coastal communities in the Great Lake region. The goal of the proposed research is to investigate the levee system safety in the Great Lake region.