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

Charity Worked with Josh Blumenstock to Identify Aid Recipients in Togo Using Mobile Phone Data

Dec 11, 2020

Jennifer Chayes at NeurIPS ’20: ML Community Must Work With Interdisciplinary Experts

Dec 14, 2020

Prof. Blumenstock’s Technology Brings Covid Aid to Togo

Dec 17, 2020

Pioneering NLP Research Examines Representation in Texas Textbooks

Oct 28, 2020

Ph.D. student Lucy Li Awarded Best Paper by the American Educational Research Association

Ph.D. Student Doris Lee Awarded 2020 Facebook Fellowship in Systems for Machine Learning

Sep 11, 2020

Lee aims to design systems that provide automated recommendations in visual data exploration and machine learning

David Bamman Wins National Endowment for Humanities Grant

Aug 13, 2020

David Bamman’s BookNLP project will receive funding from the National Endowment for Humanities (NEH) to expand its scope to include German, Japanese, Russian and Spanish languages.

Berkeley Student Develops Text-Based ID Strategies to Support UN’s Cash Assistance Programs

Jul 28, 2020

MIDS student Daniel Alvarez developed text-based similarity algorithms for the UN World Food Programme to support cash-based relief delivery.

Satellite Images, Phone Data Help Guide Pandemic Aid In At-Risk Developing Countries

Jun 2, 2020

In recent weeks, Prof. Joshua Blumenstock has worked with policymakers in Togo, Nigeria, and Bangladesh to focus the power of advanced technology on pandemic relief.

Aditya Parameswaran Awarded Best Paper at SIGMOD 2020

May 21, 2020

Assistant Professor Aditya Parameswaran has been awarded the Best Paper Award at the 2020 ACM SIGMOD/PODS Conference for his joint paper: “ShapeSearch: A Flexible and Efficient System for Shape-based Exploration of Trendlines.”

Joshua Blumenstock: Machine Learning Can Help Get COVID-19 Aid To Those Who Need It Most

May 14, 2020

As the COVID-19 crisis continues, hundreds of millions of citizens of low-income countries are being left without work or income. Assistant Professor Joshua Blumenstock discusses new methods to combat this.