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Machine Learning-Enhanced Visualization

February 21, 2018 at 10:30 AM to 12:00 PM

Location:5345 Herzberg Laboratories


Visualization is indispensable for exploratory data analysis, enabling people to interact with and make sense of data. Interaction is key for effective exploration, and is dependent on two main factors: how data is represented, and how data is visually encoded. For instance, text data may be represented as a 2D spatialization and visually encoded through graphical marks, color, and size. Typically, these factors do not anticipate how a user will interact with the data, however, which limits the set of interactions one may perform in data exploration. In this talk I will focus on how machine learning can be used to improve data representations and visual encodings for user interaction. My research is centered on building machine learning models when visualization, and in particular how a user interacts with data, is the primary objective. I will first discuss how to learn data representations for the purpose of interactive document exploration. I will demonstrate how compositional properties of neural language models, built from large amounts of text data, empower the user to semantically explore document collections. Secondly, I will show how to learn visual encodings for the purpose of exploring volumetric data. Deep generative models are used to learn the distribution of outputs produced from a volume renderer, providing the user both guidance and intuitive interfaces for volume exploration.

Short Bio:

Matthew Berger is a postdoctoral research associate in the Department of Computer Science at the University of Arizona, advised by Joshua A. Levine. Previously he was a research scientist with the Air Force Research Laboratory. He received his PhD in Computing from the University of Utah in 2013, advised by Claudio T. Silva. His research interests are at the intersection of machine learning and data visualization, focusing on the development of visualization techniques that are driven by machine learning models.