Data Science

Related Faculty

Morgan G. Ames
Assistant Professor of Practice
Alumni (MIMS 2006)
Science and technology studies; computer-supported cooperative work and social computing; education; anthropology; youth technocultures; ideology and inequity; critical data science
Daniel Aranki
Assistant Professor of Practice
Predictive medicine; artificial intelligence; machine learning; tele-health; information disclosure; privacy; security.
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Associate Professor
Natural language processing, computational social science, machine learning, digital humanities
Coye Cheshire
Professor
Trust, social exchange, social psychology, and information exchange
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Professor
Biosensory computing; climate informatics; information economics and policy
Photo of Aditya Parameswaran
Associate Professor (I School and EECS)
Data management, interactive or human-in-the-loop data analytics, information visualization, crowdsourcing, data science

Data Science news

Joshua Blumenstock

Joshua Blumenstock cautions that new digital methods of approaching issues of poverty must be used as a complement to more traditional approaches.

Anna Jacobson's data visualization "The Building Blocks of Gender Equality"

“What would it mean to do feminist data science?” This question, raised by a fellow MIDS classmate, sparked the idea for Anna Jacobson’s award-winning data visualization “The Building Blocks of Gender Equality.”

Image from The Big Sleep via The Guardian

Analysis from I School Professor David Bamman finds proportion of female authors and characters fell after 19th century, with male authors remaining ‘remarkably resistant’ to writing women.

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Machine learning and big data don’t intuitively go hand-in-hand with studies of literary fiction; however, new research from Professor David Bamman, using a machine learning algorithm and natural language processing, revealed surprising trends related to gender in novels of the 20th century.  

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Professor David Bamman’s machine-learning algorithm analyzed the presentation of gender in more than 100,000 novels.

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Position will begin July 2016; applications are due December 14.
David Bamman
Bamman’s work applies natural language processing and machine learning techniques to empirical questions in the humanities and social sciences.
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The dataset could help answer whether it’s possible to accurately use consumer-grade devices to interpret attention level in a problem-solving test. The class hopes that other researchers will be able to repeat the experiment with even larger subject pools.
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Capped by a keynote from Obama adviser John Podesta, a day-long workshop brought together the worlds of government, business, the law, and academia for what assistant professor Deirdre Mulligan called “a frank and honest conversation about our values,” and about how to balance those values with the omnipresent, often invisible collection of data about every aspect of our lives.

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