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 Computer Science)
Data management, interactive or human-in-the-loop data analytics, information visualization, crowdsourcing, data science

Recent Publications

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May 15, 2022

During the pandemic in the United States, there has been considerably more interest in home abortions than in minimally or nonclinically supported self-abortions. As access barriers to in-person abortion care increase due to legal restrictions and COVID-19–related disruptions, individuals may be turning to the internet for information and services on out-of-clinic medication abortions. Google searches allow us to explore timely population-level interest in this topic and assess its implications.

Mar 16, 2022

Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance.

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Data Science news

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.
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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