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

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MIDS student Allison Fox has been awarded the Data For Good Fellowship for her work to improve health outcomes in disadvantaged countries. 

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Her start-up, Ponder, is leading the way with a $7M round of seed financing.

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On the Nature Podcast, Professor Blumenstock discusses his research using machine learning to help deliver aid to Togo’s poorest citizens. 

Emily Aiken, doctoral student at the UC Berkeley School of Information

Governments and humanitarian groups can use machine learning algorithms and mobile phone data to get aid to those who need it most during a humanitarian crisis, we found in newly published research.

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Ph.D. student Emily Aiken and Professor Joshua Blumenstock used mobile phone data and machine learning to quickly and accurately direct the Togolese government’s COVID-19 cash assistance to its poorest residents in a first-of-its-kind study published March 16 in Nature.

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