Data Science

Related Faculty

Morgan G. Ames

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

Daniel Aranki

Assistant Professor of Practice
Predictive medicine; artificial intelligence; machine learning; tele-health; information disclosure; privacy; security.
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David Bamman

Associate Professor
Natural language processing, computational social science, machine learning, digital humanities
Coye Cheshire

Coye Cheshire

Professor
Trust, social exchange, social psychology, and information exchange
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John Chuang

Professor
Biosensory computing; climate informatics; information economics and policy
Photo of Aditya Parameswaran

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: 2. Model development
Aug 23, 2022
Impacts of heuristic parameters in PSO inverse kinematics solvers
May 16, 2022
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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

Pages

Data Science news

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MIMS Student Is Passionate About Making Data Literacy Accessible
Mar 1, 2022
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Data for Good Recipient Empowers Health Professionals Working With Infectious Diseases
Mar 23, 2022

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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Ph.D. Alum Doris Lee Wants to Democratize Data Science Tools
Mar 21, 2022

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

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Forbes Interviews Ph.D. Alum Doris Lee on New Company Ponder
Mar 17, 2022
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VentureBeat Covers the Launch of UC Berkeley Founded Data Science Startup Ponder
Mar 9, 2022
Niloufar Salehi
I School Researchers Are Using ML and AI to Explore Improvements in Medical Translation
Mar 16, 2022
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Nature Podcast: How AI Helped the Government of Togo Distribute Financial Aid.
Mar 16, 2022

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
How AI Helped Deliver Cash Aid to Many of the Poorest People in Togo
Mar 16, 2022

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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Mobile Phone Data and Machine Learning Helped Togo Government Provide Assistance
Mar 16, 2022

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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Joshua Blumenstock’s Research on Micro-Estimates of Wealth Data Covered by SciDev
Jan 25, 2022

Pages

Research