Modeling Dynamic Behaviors within Population - Tavabi, Nazgol (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
The abundance of temporal data generated by mankind in recent years gives us the opportunity to better understand human behaviors along with the similarities and differences in groups of people. Better understanding of human behaviors could be very beneficial in choosing strategies, from group-level to society-level depending on the domain. This type of data could range from physiological data collected from sensors to activity patterns in social media. Identifying frequent behavioral patterns in sensor data could give more insight into the health of a community and provoke strategies towards improving it; By analyzing patterns of behaviors in social media, platform's attributes could be adjusted to the user's needs. This type of modeling introduces numerous challenges that varies depending on the data. The goal of my doctoral research is to introduce ways to better understand and capture human behavior by modeling individual's behaviors as time series and extracting interesting patterns within them.