Human-Computer Interaction Thesis Defense

  • Gates Hillman Centers
  • Traffic21 Classroom 6501
  • Ph.D. Student
  • Human-Computer Interaction Institute
  • Carnegie Mellon University
Thesis Orals

Computational Method for Understanding Complex Human Routine Behaviors

The ability to collect and store large amounts of human behavior traces data collected from various sensors on people’s personal, mobile, and wearable devices, as well as from smart environments, offers a new source of data to study human behavior at scale. However, existing Human-Computer Interaction (HCI) behavior sensemaking methodologies do not lend themselves to studying behaviors from such large multivariate, heterogeneous, and unlabeled datasets. On the other hand, computational modeling has been used to successfully explore and understand complex systems in other fields (e.g., climate change modeling). Inspired by such prior work, I treat behaviors stored in large behavior logs as a complex system that we can capture in a computational model of human behavior.

In this thesis, I focus on behaviors in the domain of human routines that people enact as sequences of actions they perform in specific situations, which we call behavior instances. Computational models allow us to explore behaviors by manipulating model variables and simulating and detecting different kinds of behaviors (otherwise known as “asking what-if questions”). I present a probabilistic computational model of human routine behaviors, that can describe, reason about, and act in response to people’s behaviors. I ground the model in a holistic definition of human routines to constrain the patterns it extracts from the data to those that match routine behaviors. Model training estimates the likelihood that people will perform certain actions in different situations in a way that matches their demonstrated preference for those actions and situations in behavior logs. I leverage this computational model of routines to create various tools to aid stakeholders, such as domain experts and end users, in exploring, making sense of, and generating new insights about human behavior stored in large behavior logs in a principled way

Thesis Committee: 
Anind K. Dey (Co-Chair)
Jennifer Mankoff (Co-Chair)
Aniket Kittur
Eric Horvitz (Microsoft)

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