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 Computer Science)

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

Recent Publications

Differential Privacy for Black-Box Statistical Analyses

May 22, 2023

Monkeypox Outbreak Analysis: An Extensive Study Using Machine Learning Models and Time Series Analysis

Mar 30, 2023

Survivability of industrial internet of things using machine learning and smart contracts

Mar 1, 2023

SDN and application layer DDoS attacks detection in IoT devices by attention‐based Bi‐LSTM‐CNN

Mar 1, 2023

A transfer learning approach for detecting offensive and hate speech on social media platforms

Feb 2, 2023

Time series analysis and anomaly detection for trustworthy smart homes

Sep 11, 2022

Exchange and the Creation of Trust and Solidarity across Cultures

Sep 6, 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

Data Science news

Santamaria Ots locating cities in need of relief supplies.

“Data for Good” Fellowship Awardee Aims to Empower Puerto Rican Citizens

Sep 18, 2019

In the summer of 2019, Daniel Santamaria Ots received the Jack Larson Data for Good Fellowship for his research in Puerto Rico.

Marti Hearst is a 2019 Bloomberg Data Science Research Grant Winner

Jun 11, 2019

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

Josh Blumenstock Uses Phone Data to Reveal Socioeconomic Status

Feb 23, 2019

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.