Data Science

Related Faculty

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Assistant Professor
Natural language processing, computational social science, machine learning, digital humanities
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Assistant Professor
Alumni (PhD 2012)
Development Economics, Data Science, Econometrics, Machine Learning, ICTD
Coye Cheshire
Professor
Trust, social exchange, social psychology, and information exchange
John Chuang
Head of School, Associate Dean, and Professor
Bio-sensory computing; brainwave authentication; information economics and policy
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Assistant Professor (I School and EECS)
Data management, interactive or human-in-the-loop data analytics, information visualization, crowdsourcing, data science
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Assistant Professor (I School and Graduate School of Education)
Learning Analytics, Digital Learning Environments, Machine Learning
Steve Weber
Faculty Director, Center for Long-Term Cybersecurity and Professor (I School and Dept. of Political Science)
International politics, international business, and the information economy; Cybersecurity; Behavioral economics within Information Systems

Data Science news

Professor Marti Hearst

Professor Marti Hearst is one of six recipients of a Bloomberg Data Science Research Grant for research on Unsupervised Abstractive News Summarization.

Josh Blumenstock at the Artifical Intelligence for Economic Development conference

Prof Blumenstock received the Faculty Award for Research in the Public Interest for his research at the intersection of machine learning and development economics.

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.

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