Frank Shipman

Associate Director,
Center for the Study of Digital Libraries

Professor,
Department of Computer Science
Texas A&M University
College Station, Texas 77843-3112

Detailed Information:
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Vitae

Biography:

Frank Shipman has been pursuing research in the areas of hypermedia, computer-supported cooperative work, multimedia, computers and education, and intelligent user interfaces since 1987. Frank's work at Baylor College of Medicine, the University of Colorado, Xerox PARC, and Texas A&M University investigates the design and use of media combining informal and formal representations and methods for supporting incremental formalization.

Frank helped found the field of spatial hypertext and helped design and develop a number of collaborative hypermedia systems including the Virtual Notebook System, the Hyper-Object Substrate, VIKI , the Visual Knowledge Builder, Walden's Paths, and Hyper-Hitchcock.

Frank has been PI or Co-PI on more than $6 million in grants including more than $3.8 million from NSF, more than $1.1 million from other competitive sources including DARPA and the intelligence community, and more than $800,000 from industry including Microsoft, Google, and Hewlett Packard. His research has resulted in more than 100 refereed publications including three best paper awards and six other papers nominated for best paper awards at ACM and IEEE conferences.

Teaching Schedule (subject to change):

  • Fall 2017: Intelligent User Interfaces (CSCE 634)
  • Spring 2018: Computers and New Media (CSCE 445/656)
  • Fall 2018: Computer Supported Cooperative Work (CSCE 672)
  • Spring 2019: Computers and New Media (CSCE 445/656)


  • Current/Recent Projects:

    Social Media Ownership
    As more and more content is created and shared within social media applications and services, ownership is becoming increasingly difficult to determine. Twitter conversations, Amazon reviews and Facebook commentary are examples of how the creative activities of many join together in ways that challenge traditional concepts of content authorship and ownership.

    Studying people's beliefs and practices regarding ACM DL Author-ize service the ownership and reuse of social media. This includes individual studies of content from Twitter, photographs, videos, reviews, games, and social networks. Our method for doing this work won the best paper award for WebSci 2013. See the introductory video at YouTube or on Vimeo.

    Sign Language Digital Library (SLaDL)
    Access to sign language (SL) content poses a number of challenges for existing ingestion, location, and management capabilities. This project explores the potential for automatic processing of content combined with community involvement and novel user interfaces to improve the sign language community's access to content.

    Activities of the project include an assessment of the difficulty of SL access, techniques for detecting SL activity using video features, and techniques for distinguishing between different sign languages using video features. The project also explores the more general problem of infrastructure for identifying community-oriented video libraries within large video sharing sites like YouTube.

    Data Triage and Analysis
    Collecting, triaging, and analyzing heterogeneous data is a challenge due to the separation of data by media or type and the separation of activity across tools for different phases of the overall analysis process. This project applies the lessons learned from prior work supporting text and image analysis in human-authored visual workspaces to the problem of heterogenous data analysis.

    An initial focus has been the development of techniques for interacting with and visualizing time-stream data in visual workspaces and techniques for recommending data objects based on prior user activity as seen in the design of PerCon.

    Prediction Games
    Games involving the prediction of future performance or activity (e.g. Fantasy Sports) encourage engagement with data. This project investigates the practices of players of existing prediction games, the development of a domain-independent prediction game engine, and the design of prediction games in new domains. We are particularly interested in the application of such games to data science education.

    This project began by understanding the history of and developing a model of fantasy sports. This involves understanding the practices of fantasy sports players. Based on this analysis we have developed initial data-driven prediction games to motivate data analysis skills and rich domain knowledge.