NRT Student Research Summit

NRT Student Research Summit Fall 2019

CAITLIN O'LOUGHLIN: Designing and Implementing a Resilient Trust Framework for P2PR2P

Caitlin will discuss my contributions to P2PR2P, an application used to support a distributed system of aid delivery, which include designing a resilient trust framework for users.

AMY BURTON: A Mean-Risk Decision Model for Transportation Network Protection

Using stochastic programming, we look at allocating resources in a transportation network in a way that best hedges against future damage.

ANDRE APOSTOL: ARTISAN: Agent-Based Resilient Transportation Infrastructure with Surrogate Adaptive Networks

The implementation of adaptive timing signals through communication between autonomous vehicles and intelligent traffic signals in an agent-based environment can improve city intersection traffic flow and create a safer environment for the transportation infrastructure.

JON LONSKI: Leveraging Social Media Data For Disaster Relief Management

Recent natural disasters like Hurricane Sandy (2012) and Hurricane Harvey (2017) have left survivors stranded without basic supplies necessary for survival. During such disasters, nonprofit organizations receive donations of supplies, and then must transport them to survivors. I will discuss the potential of machine learning to analyze Twitter data (tweets) to develop improved survivor demand forecasts.

CARL EHRETT: Robust design under uncertainty with counterfactual Bayes

Established methods of computer model calibration can be reconceptualized as tools for design, producing results that include information about design tolerances and quantify all relevant sources of uncertainty.

ISSAC HAYES:Improving Multi-Scale Flash Flood Modeling Using NEXRAD Weather Radar

Flash floods are floods resulting from short duration, high intensity rainfall events. Flash floods are a difficult phenomenon to model at varying spatiotemporal scales due to the nature of flash flood producing storm events. Integrating NEXRAD weather radar clustering algorithms into hydrologic models provides a promising technique for improving model resolution across spatial scales.

Appreciate

Credits:

Created with images by Hannes Ri - "untitled image" • Chris Yang - "untitled image" • Pexels - "architecture buildings cars" • WikiImages - "hurricane tropical cyclone typhoon" • Clément H - "JavaScript in progress"