Statistics and Data Science Seminar: "Improving Human-Machine Partnership in Visual Data Analysis"

Abstract: There is a fast-growing interest in analyzing user interaction to create adaptive visualization systems that can assist or collaborate on data analysis. However, the first step to creating such tools is understanding the user. Dr. Ottley’s work uses an observational learning framework, akin to humans learning concepts like language and behavior naturally through observations, often with no explicit feedback. The goal is to enable computers to infer user attributes and strategies by observing their interactions with a system. In this talk, Dr. Ottley summarizes her lab's work on user modeling for data visualization and gives a snapshot of the current research achievements and what is possible in the near and distant future. Then, she presents techniques for capturing and predicting user behavior, focusing on inferring attention, personality, biases, and knowledge by analyzing log data. Finally, Dr. Ottley highlights the significant roadblocks and future directions for visualization research.

Host: Debashis Mondal