nanoHUB

The project focuses on enhancing user experience on nanoHUB.org, a renowned online platform for nanotechnology and related disciplines, offering simulation tools, educational resources, and a vibrant community.

The primary objective is to refine the classroom cluster detection system with advanced algorithms, considering usage patterns and cluster mixtures. Our incremental approach involves concentrating on early data usage to predict emerging trends in classroom dynamics. Proactively identifying behavior patterns aims to provide predictive insights, optimizing the user experience and resource allocation on Nanohub.

The evaluation phase includes comparing the effectiveness of new methods with existing cluster algorithms, identifying strengths, weaknesses, and potential synergies for a more efficient system. In an optional phase, we’ll explore identifying prominent institutions within classroom clusters, providing insights into Nanohub’s educational landscape and user demographics.

My role involves assisting code development for the clustering algorithm, statistical analysis, and data visualization on user activity. I interpret the output and present it to the team to enhance understanding. Weekly meetings with mentors Gerhard Klimeck and Daniel Meija ensure effective communication and project progress, a collaborative effort crucial in steering towards our goals.