Sharing your data profile on the Data Nexus website give us the opportunity to highlight Elon data initiatives and to promote Elon data driven collaborations.

Submit your Data Profile HERE!

Elon Faculty and Staff Data Profiles

Data Highlight: Deep learning, Machine Learning, Inferential Statistics, Multi-level Analysis, Structural Equation Modelling, Social Network Analysis, Text Analysis, Meta-Analysis, Experimental Sampling Methodology.
Keywords: Deep learning, Machine Learning, Inferential Statistics, Multi-level Analysis, Structural Equation Modelling, Social Network Analysis, Text Analysis, Meta-Analysis, Experimental Sampling Methodology
Collaborations: I’d be happy to collaborate with the faculty in their deep learning, machine learning, or other data intensive projects.

Data Highlight: I use spatial data to study how species move because of climate change and other human alterations to the environment. I also teach field techniques classes where students collect and analyze data around biodiversity. Finally, I am interested on teaching practices around grading and collect data on that.
Keywords: Biogeography, climate change, invasive species, grades
Collaborations: I am always interested in collaborators who are interested in using spatial data and other kinds of data in conservation research!

Data Highlight: We collect environmental and experimental data from field and lab tests to determine efficacy of stormwater treatments. We also use historical and future climate projection data to model and predict water quality concerns.
Keywords: Climate, water quality, chemistry
Collaborations: Looking for anyone interested in quantifying and/or mitigating climate and water pollution concerns through lab experimentation, modeling, and/or field-testing.

Data Highlight: In my role as an assistant professor in the Communication Design department, I integrate data extensively in my research, teaching, and daily activities. My research primarily involves collecting and analyzing political visuals such as candidate logos, yard signs, and other graphic elements. I use AI and quantitative content analysis, supported by coders, to assess these visuals. This approach helps in understanding the impact and effectiveness of visual communication in politics. Additionally, I am venturing into the realm of visual generative AI, exploring its potential applications and implications. In my teaching, I guide students in the creation of infographics and various data visualizations, emphasizing the importance of data-driven design decisions. Daily, I employ data to refine my research methods, develop curriculum, and provide insightful feedback to my students.
Keywords: AI in Visual Analysis, Quantitative Content Analysis, Political Visuals, Candidate Logos, Yard Signs, Graphic Elements, Visual Generative AI, Infographics, Data Visualization, Communication Design, Experimental
Collaborations: I envision numerous collaborative opportunities with faculty, staff, and students centered around data-driven projects. For faculty, I am eager to explore interdisciplinary research that merges visual communication with political science, leveraging our collective expertise to yield richer insights. Collaborative efforts could include co-authoring research papers, developing joint curriculum modules, or organizing workshops on AI and visual analysis. For staff, particularly those in IT and data services, I see collaboration in the development and implementation of AI tools for visual content analysis. This could involve creating specialized software or platforms that facilitate the research and teaching processes. With students, I am particularly enthusiastic about mentoring those interested in infographics and data visualization. Collaborative projects could involve real-world data, where students analyze and visualize political campaigns, creating compelling visual narratives. This hands-on experience not only enriches their learning but also contributes valuable insights to our collective research endeavors.