Biography

My research is broadly focused on building statistical models to understand and predict complex behavioral processes. Toward this end, I develop and disseminate machine learning methods that can be used to extract insights from various types of behavioral data including:

• smartphone and wearable sensor data,
• electronic health records,
• large-scale surveys,
• standardized tests,
• social media usage data,
• and more.

I’m particularly fascinated by the field of deep learning, which includes an array of powerful methodological tools for constructing realistic models of complex phenomena.

As a Ph.D. student, I was supported for three years by a National Science Foundation Graduate Research Fellowship. Before that, I built machine learning models to detect at-risk undergraduate students for the Finish Line Project, an inter-departmental initiative to improve retention of first-generation college students at the University of North Carolina at Chapel Hill.

For more information about my work, please visit my personal website: https://cjurban.github.io/