David Schloen & Sandra Schloen, The Oriental Institute
May 23, 2014
Searle 240A

Computation Institute Presentation - Data Lunch Seminar (DLS)

Speakers: David Schloen, Associate Professor of Archaeology, The Oriental Institute and Department of Near Eastern Languages and Civilizations
Sandra Schloen, Research Database Specialist and Manager of the OCHRE Data Service, The Oriental Institute
Host:  Tanu Malik 

Date:  May 23, 2014
Time: 12:00 PM - 1:00 PM
Location: University of Chicago, Searle 240A, 5735 S. Ellis Ave. 

OCHRE: An Online Cultural and Historical Research Environment

Kate Von Holle, Trudy Vincent, and Matt Greenwald, University of Chicago Office of Federal Relations
May 22, 2014
Searle 240A, University of Chicago & Adobe Connect

Members of the Office of Federal Relations will give an overview of the Office and describe how it supports the work of the University by identifying or creating opportunities to promote University interests to the White House, executive branch agencies, members of Congress, and other national opinion leaders and stakeholders. 

Gary An, University of Chicago Medicine & Rick Stevens, Argonne National Laboratory
May 21, 2014
Searle 240A, University of Chicago & Adobe Connect

We are pleased to present the 5th installment of the Inside the Discovery Cloud Speaker Series, on Life Sciences:

Gary An, Associate Professor of Surgery, University of Chicago Medicine; CI Senior Fellow
"Beyond Big Data: Making biomedical science scale through the use of in silico dynamic knowledge representation”

May 19, 2014 to May 20, 2014
Theater, Ida Noyes Hall

Information, Interaction, and Influence
Joint University of Chicago-Digital Science Workshop on Research Information Technologies and their Role in Advancing Science
Attendance by registration.  Register at http://bit.ly/1g2s0bX

Yuening Hu
May 02, 2014
Searle 240A

Topic models are a useful and ubiquitous tool for discovering the main themes (namely topics) of a corpus, and have been successfully applied in various research areas. However, the discovered topics are not always meaningful: some topics confuse two or more themes into one topic; two different topics can be near duplicates; and some topics make no sense at all. For many users in computational social science, digital humanities, and information studies-who are not machine learning experts- existing models and frameworks are often a "take it or leave it" proposition.