Develop a machine learning (ML) pipeline for the automated interpretation of Ground Penetrating Radar (GPR) images to identify and classify subterranean features in the Appalachian region. The project aims to increase the efficiency and accuracy of GPR data analysis for applications in infrastructure development, environmental monitoring, and safety assessments.

Utilize machine learning techniques to automate the interpretation of LiDAR data for detecting subsurface and near-surface geological features in the Appalachian region. This project focuses on combining LiDAR’s high-resolution capabilities with ML to identify voids, faults, and shallow subsurface anomalies.

To design and evaluate a reverse logistics framework that addresses the collection, transportation, sorting, and recycling of EV batteries, integrating economic, environmental, and operational considerations. The research will focus on identifying optimal network configurations to maximize resource recovery and minimize environmental impact while adapting to varying regional and market dynamics.

Evaluate the role of technology solutions such as drones and telemedicine, assess the impact of helicopter availability on patient outcomes, and develop tailored emergency response protocols to reduce response times and improve care in rural areas. Examine how these interventions influence outcomes across diverse socio-economic, socio-technical, and demographic contexts.

Being around natural environments and green spaces such as greeneries, trees, water bodies can significantly reduce stress and promote relaxation. With their low-stimulus and calming nature, they can help people recover from mental fatigue. They also make outdoor physical activities more accessible to communities, in addition to providing a place for social connection and a sense of belonging. Overall, careful environmental infrastructure planning (e.g., parks, urban forests, trails) is beneficial for individuals and communities. This research project involves creating a simulation game about how human wellbeing is affected by and can be enhanced through careful green environment infrastructure planning, in addition to testing the final product with users to measure its effectiveness in communicating these ideas.

To design and implement a restoration framework for power infrastructure that integrates system-level analysis, resource optimization, and adaptive decision-making. The framework will focus on prioritizing critical infrastructure, optimizing resource allocation, and leveraging advanced technologies to improve restoration efficiency under diverse disruption scenarios and operational constraints.

The proposed research will explore the potential of several untested anionic azo dyes to be used as agents in the functionalization process. Alongside the faculty mentor, the participant will get hands-on training in the laboratory, collect flowrate samples, perform ultraviolet visible light spectroscopy on samples, and present findings when possible.