In September we had two InfoSkeeing Lab successfully defend their dissertations!

On September 7^th, our very own Zaid Matni successfully defended his dissertation, “The Influence of Network Structures and Information Seeking Uncertainty on Information Seeking Behavior” in front of a demanding panel of fellow scholars, marking our 6^th PhD Graduate of the lab.

Zaid’s dissertation explores quantifiable behavioral dynamics of individuals who are seeking information using different social network structures over time. His study utilized a custom-built Web-based tool that simulates an information-seeking scenario via various network structures and had participants utilize it to achieve a stated goal of collecting answers to questions from others in their network. The tool allows a finite amount of interactions, thus limiting the participants’ engagements to a defined set of allowable actions. His dissertation contributed to the theories of information seeking in social network environments, as well as to social network theory as it pertains to human information behavior. Additionally, it served as another method to study human behavior through the lens of social networks by providing them with a sophisticated computer-mediated platform to collect log-based data of human behavior in simulated networked environments.

Matt Mitsui became our 7^th PhD Graduate of the lab on September 20^th! He successfully defended his dissertation, “Adopting a Graphical Perspective in Interactive Information Retrieval Research” in front of a demanding panel of fellow scholars.

Matt’s dissertation demonstrates that task characteristics, user characteristics, and behaviors should be empirically studied as a network of dependencies. It expands empirical work using graphical modeling, which can uniquely capture phenomena such as mediation and conditional independence.  His research empirically shows when knowledge about behavior and certain task characteristics can be used to learn about other aspects of the task. Additionally, it shows how task and user characteristics simultaneously affect behavior while potentially affecting each other. Specifically applying path analysis and Bayesian structure learning, results are shown to agree well with past literature and to also extend our understanding of the information seeking process.

Congratulations to Dr. Matni and Dr. Mitsui; we wish you the best of luck in the next chapter of your lives!