How do we Interpret Climate Change with Big Data?

James Ascher (English) and Bommae Kim (Psychology)

This project investigated discourse around climate change, where environmental data play a central role in arguments. Specifically, Ascher and Kim explored rhetorical patterns in how climate change contrarians argue against climate change.

Exploring the Effects of Violence Against Women on Health Care Utilization and Academic

Xiaoqian Liu (Systems and Information Engineering) and Colleen Sanders (Nursing)

This project proposed a comprehensive way to measure academic performance of college-aged women who were victims of sexual and intimate partner violence by collecting data from emergency rooms, a student enrollment system and a course evaluation website. In addition to traditional regression and classification methods, Liu and Sanders applied distance metric learning methods to discovering a set of evaluation metrics of academic progress and testing hypotheses on the impact of abuse history and health care utilization. 

A Data Driven Approach for Uncovering Patterned Life Cycles of Ideas and Methods within the Sciences

Claire Maiers (Sociology) and Nicholas Napoli (Systems and Information Engineering)

This project aimed to uncover the patterned ways by which ideas move through social systems. They examined the ways in which concepts and methods diffuse, endure and decline within academic fields using the JSTOR database, which contains more than 4.5 million academic articles published between 1900 and 2015.

Multi-Agent Modeling and Analysis of Large Scale Brain Networks with a Big fMRI Data Set

Marlen Gonzalez (Psychology), Shize Su (Electrical and Computer Engineering), and Qiannan Yin (Statistics)

This project analyzed large-scale brain networks involved in the social regulation of emotion, using both statistical methods and engineering tools. Using data from a social support functional neuroimaging study, Gonzalez, Su and Yin modeled the brain as a dynamic network, with nodes referring to different brain regions and lines representing the interactions between each pair of brain areas.

Violent Beliefs, Violent People?  Data Driven Analysis of Religious Belief and Social Action in Heian Japan

Alex Pape (Systems and Information Engineering) and Emily Thomas (East Asian Studies)

This project sought to gain an understanding of religious violence using machine learning tools. Pape and Thomas created a new model that uses both the traditional data-driven methods used by data scientists and the contextual narratives used by humanities scholars to better model and understand complex social phenomena.

Data Driven Design of Movement and Sound

Lin Bai (Electrical and Computer Engineering) and Jon Bellona (Music)

This project, through capturing and analyzing human movement and sound data, and studying the correspondences that articulate how humans move, developed robotic movements synchronized with perceptually designed sounds as a way to make robotic movements more expressive and thus increase the level of human perception of quality in robotic movement. 

Applying Machine Learning to Text Communications to Model Suicide Risk in Real-time

Jeffrey Glenn (Psychology) and Alicia Nobles (Systems and Information Engineering)

This project applied text mining techniques to social media and personal communications to identify features indicative of heightened suicide risk. Glenn and Nobles' research is novel because it applied text analytic approaches to communications temporally, allowing not only for prediction of suicide risk, but also for insight into how these predictive attributes change as an individual draws closer to their suicide attempt.