To develop an AI-enhanced 3D reconstruction workflow that integrates UAV imagery with existing aerial and satellite data to generate high-resolution, real-time georeferenced models of coastal and watershed features in the southern Lake Michigan region. To apply this system to monitor and quantify environmental changes—such as shoreline erosion, dune morphology, stormwater runoff, and infrastructure vulnerability—before, during, and after extreme weather events or seasonal transitions. To evaluate the performance and accuracy of state-of-the-art reconstruction methods (VGGT, MASt3R, DUSt3R) for coastal applications, using ground-truth data (e.g., GNSS, LiDAR) to validate outputs and assess model limitations under varying conditions. To create an open-access toolkit and decision-support platform—including a web-based dashboard and immersive VR/MR interface—that enables stakeholders to visualize 3D results and extract actionable metrics (e.g., erosion rates, flood extent, asset risk). To engage with community stakeholders and IISG outreach specialists from project inception to ensure research findings are translated into practice through training workshops, user guides, and integration with local planning and public outreach efforts.

The overarching objective of this proposed study is to advance a systems-level understanding of nutrient pollution dynamics in the Lake Michigan Basin. This will be achieved by integrating spatial analysis, nutrient surplus-export coupling, and scenario-based modeling to support targeted, climate-resilient watershed management strategies. Objective 1: Classify watershed typologies based on surplus–export relationships and identify nutrient pollution hotspots. Objective 2: Develop a conservation framework that integrates hotspot identification, factor analysis, and targeted interventions. Objective 3: Evaluate the effectiveness of management scenarios under projected climate and hydrologic conditions using modeling approaches.

The ultimate goal of this proposed study is to develop a near-real-time system for flood inundation mapping and flood depth estimation, finally enabling timely assessments of community transportation network disruptions during flood events. As part of this effort, three key anticipated outcomes will be delivered: (1) An off-the-shelf flood inundation mapping model; (2) an algorithm that estimates flood depth by integrating flood inundation maps with digital elevation models, and (3) an algorithm that assesses transportation system disruptions by combining flood information with real-world road network data. 

The primary objective of this study is to investigate how the home sharing economy (HSE), led by the rapid growth of short-term rental (STR) platforms such as Airbnb, influences housing market dynamics in the Lake Michigan region. While STRs have contributed to local economic growth through increased tourism and income opportunities, they have also raised concerns about housing affordability and community sustainability, particularly in urban and coastal areas where STR activity is concentrated. This research seeks to empirically examine the extent to which STR supply affects key housing market outcomes—specifically, home values, rental prices, and transaction volumes—using a panel dataset organized at the zip-code-by-month level. Recognizing the regional specificity of STR impacts, the study aims to identify not only whether STR activity influences housing markets, but also under what neighborhood-level conditions these effects are more pronounced. By exploring heterogeneity in STR effects across different community types (e.g., high-tourism vs. residential, renter-dominated vs. owner-dominated), the study generates a nuanced understanding of STR-driven housing dynamics. Furthermore, the study conducts counterfactual simulations to estimate how hypothetical supply-side regulations (such as annual STR caps) could alter housing market outcomes in different neighborhoods. These insights will be particularly relevant for local governments in the Lake Michigan area, where STR regulation remains limited or absent. Ultimately, this research contributes to evidence-based policymaking by offering targeted, data-driven recommendations for balancing the economic benefits of STRs with the long-term housing needs and social sustainability of local communities.

The primary objective of this study is to establish a high-resolution, spatially explicit understanding of total nitrogen (TN) and total phosphorus (TP) export across the Lake Michigan Basin, spanning the period from 2001 to 2020. By calibrating and integrating advanced modeling techniques, the Weighted Regression on Discharge, Time, and Season with Kalman filtering (WRTDS-K) method and the Spatially Referenced Regressions On Watershed attributes (SPARROW) model, this work seeks to (1) quantify spatiotemporal trends in TN and TP loading and identify localized hotspots of nutrient export, and (2) disentangle the relative contributions of human activities (e.g., fertilizer application, wastewater management, conservation practices) and hydrological variability (e.g. precipitation, temperature). Ultimately, these objectives aim to support policymakers, stakeholders, and local communities in developing more effective nutrient management plans and ensuring the sustainability of vital freshwater resources in and around the Lake Michigan Basin.

This project aims to evaluate the feasibility of using a conductive CNF-TPU foam as a wave-responsive material for low-power aquatic energy harvesting. The specific objectives are:

• To optimize and characterize the electromechanical performance of CNF-TPU foam under cyclic loading in both dry and submerged conditions.
• To fabricate a floating prototype and simulate lake-like wave motion to assess signal response and repeatability.
• To integrate a basic energy harvesting circuit and benchmark the output, identifying its potential to power low-energy electronics in freshwater environments.