- About
- Courses
- Research
- Computational Social Science
- Critical Data Studies
- Data Science
- Economics and Information
- Education Technology
- Ethics, Law and Policy
- Human-Computer Interaction
- Human-Robot Interaction
- Incentives and Computation
- Infrastructure Studies
- Interface Design and Ubiquitous Computing
- Natural Language Processing
- Network Science
- Social Computing and Computer-supported Cooperative Work
- Technology and Equity
- People
- Career
- Undergraduate
- Info Sci Majors
- BA - Information Science (College of Arts & Sciences)
- BS - Information Science (CALS)
- BS - Information Science, Systems, and Technology
- MPS Early Credit Option
- Independent Research
- CPT Procedures
- Student Associations
- Undergraduate Minor in Info Sci
- Our Students and Alumni
- Graduation Info
- Contact Us
- Info Sci Majors
- Masters
- PHD
- Prospective PhD Students
- Admissions
- Degree Requirements and Curriculum
- Grad Student Orgs
- For Current PhDs
- Diversity and Inclusion
- Our Students and Alumni
- Graduation Info
- Program Contacts and Student Advising
Jason Chuang received his Ph.D. in Computer Science from Stanford University, and is now a post-doctoral researcher at the University of Washington. He investigates how people work with data and each other to accomplish real-world analysis tasks. By examining how automated techniques and users' actions jointly contribute to the analytic process, he develops improved visualizations and analysis algorithms. His research draws on work from multiple disciplines including information visualization, human-computer interaction, machine learning, and natural language processing.
Title: Designing Visual Analysis Methods
Abstract: Scientific discoveries today are increasingly powered by analysis of massive datasets. As our unprecedented access to data continues to grow, how do we build analysis tools to support scientific breakthroughs of tomorrow?My research focuses on the design of interactive visualizations, statistical models, and integrated analysis workflows to enable people and algorithms to work in tandem to yield insights from complex data.In this talk, I first describe my experiences developing a variety of text analysis tools. I present guidelines for creating effective model-driven visualizations, and demonstrate 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 show that developing and deploying machine learning techniques can be a challenging analysis task in itself, which benefits from the application of visual analytics. 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.