Data Science

Related Faculty

Morgan 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.

David Bamman

Associate Professor

Natural language processing, computational social science, machine learning, digital humanities

Coye Cheshire

Professor

Trust, social exchange, social psychology, and information exchange

John Chuang

Professor

Biosensory computing; climate informatics; information economics and policy

Aditya Parameswaran

Associate Professor (I School and EECS)

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

Recent Publications

Mitigating bias in radiology machine learning: 3. Performance metrics

Aug 23, 2022

Impacts of heuristic parameters in PSO inverse kinematics solvers

May 16, 2022

Exploring Google Searches for Out-of-Clinic Medication Abortion in the United States During 2020: Infodemiology Approach Using Multiple Samples

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.

Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications

May 1, 2022

Machine learning and phone data can improve targeting of humanitarian aid

Mar 16, 2022

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

A Novel Cloud Architecture for Internet of Space Things (IoST)

Jan 18, 2022

Micro-Estimates of Wealth and Poverty for all Low- and Middle-Income Countries

Jan 3, 2022

Self-organizing maps and Bayesian regularized neural network for analyzing gasoline and diesel price drifts

Jan 3, 2022

A sentiment-based modeling and analysis of stock price during the COVID-19: U- and Swoosh-shaped recovery

Dec 26, 2021

Leveraging Differential Privacy While Attending to Social and Political Commitments

Dec 15, 2021

Data Science news

Josh Blumenstock at the Artifical Intelligence for Economic Development conference

Josh Blumenstock Honored for Research in the Public Interest

Apr 16, 2019

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: The Knowns and Unknowns of Big Data and Poverty Alleviation

Mar 18, 2019

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"

MIDS Student’s Feminist Visualization wins Data Science Challenge

Jan 29, 2019

“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

The Guardian Notes David Bamman’s Findings on Representation of Women in Literature

Feb 19, 2018

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.

Big Data Meets Literary Analysis: Digital Humanities Research at the I School

Mar 21, 2018

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.

The Economist Explores David Bamman’s Algorithmic Literary Analysis

Mar 8, 2018

Big Data Reveals the Shrinking Role of Women in Novels

Feb 14, 2018

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

School of Information Hiring New Professor in Human-Centered Data Science

Oct 8, 2015

Position will begin July 2016; applications are due December 14.

New Professor David Bamman is a Scholar of Computational Humanities and Social Sciences

Sep 1, 2015

Bamman’s work applies natural language processing and machine learning techniques to empirical questions in the humanities and social sciences.

School of Information Releases First-of-its-Kind Synchronized Brainwave Research Dataset

Mar 18, 2015

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.