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Title: Interactive Visual Text Analytics
Hannah Kim
School of Computational Science & Engineering
College of Computing
Georgia Institute of Technology
Date: Wednesday, November 18, 2020
Time: 9am - 11am EST
Location (remote via Bluejeans): https://bluejeans.com/218309196
Committee
Dr. Haesun Park - Advisor, Georgia Institute of Technology, School of Computational Science and Engineering
Dr. Alex Endert - Georgia Institute of Technology, School of Interactive Computing
Dr. Polo Chau - Georgia Institute of Technology, School of Computational Science and Engineering
Dr. Chao Zhang - Georgia Institute of Technology, School of Computational Science and Engineering
Dr. Nan Cao - Tongji University, College of Design and Innovation and College of Software Engineering
Abstract
Human-in-the-Loop machine learning leverages both human and machine intelligence to build a smarter model. Even with the advances in machine learning techniques, results generated by automated models can be of poor quality or do not always match users' judgment or context. To this end, keeping human in the loop via right interfaces to steer the underlying model can be highly beneficial. Prior research in machine learning and visual analytics has focused on either improving model performances or developing interactive interfaces without carefully considering the other side.
In this dissertation, we design and develop interactive systems that tightly integrate algorithms, visualizations, and user interactions, focusing on improving interactivity, scalability, and interpretability of the underlying models. Specifically, we present three visual analytics systems to explore and interact with large-scale text data. First, we present interactive hierarchical topic modeling for multi-scale analysis of large-scale documents. Second, we introduce interactive search space reduction to discover relevant subset of documents with high recall for focused analyses. Lastly, we propose interactive exploration and debiasing of word embeddings.
