TOCS Event-Silicon Valley Campus - Carnegie Mellon University

TOCS Event-Silicon Valley Campus - Carnegie Mellon University

TOCS Event


Jason Chuang

Postdoctoral Researcher, Computer Sciences - Stanford University


June 11, 1:30 pm



CMUSV, Rm 118 [directions]

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Title: Designing Visual Analysis Methods

Scientific discoveries today are increasingly powered by analysis of massive datasets. As our already unprecedented access to data continues to grow, how do we design analysis tools to support scientific breakthroughs of tomorrow?

My research focuses on the co-design of interactive visualizations and statistical analysis algorithms to improve the process of data analysis -- to create effective workflows where human cognition and algorithms work in tandem to gain insights about large and complex data.

In this talk, I describe my experiences developing a variety of text analysis tools. I first present design principles for creating effective model-driven visualizations, and show that model design is just as critical as visual design in determining the effectiveness of a tool. I then examine the effective design of statistical models. I demonstrate that the selection and evaluation of machine learning methods can be a challenging analysis task in itself, which benefits from the application of visual analytics. Finally, applying a human-centered iterative design method to statistical topic modeling, I contribute methods, tools, and frameworks that allow users to more efficiently utilize domain expertise to assess model outputs and explore modeling options. My approach improves our understanding of topic modeling techniques, and leads to tools and models that are responsive to user needs and support domain-specific applications.

Speaker Bio:

Jason Chuang received his Ph.D. in Computer Science from Stanford University. His thesis work, under the advisory of Jeffrey Heer and Christopher D. Manning, examines the design of model-driven visualizations and promotes a human-in-the-loop approach to machine learning. His research draws on work from multiple disciplines including information visualization, visual analytics, human-computer interaction, machine learning, and natural language processing.