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
Josh Blumenstock
Associate Professor
Alumni (PhD 2012)
Development Economics, Data Science, Machine Learning, Econometrics, ICTD
Coye Cheshire
Professor
Trust, social exchange, social psychology, and information exchange
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Professor
Bio-sensory computing; brainwave authentication; 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
Steven Weber
Professor (I School and Dept. of Political Science); Faculty Director, Center for Long-Term Cybersecurity
International politics, international business, and the information economy; Cybersecurity; Behavioral economics within Information Systems

Recent Publications

teaser image of book cover - O'Reilly Media - 97 Things About Ethics Everyone in Data Science Should Know
Aug 2, 2020

When you go to a new healthcare clinic in the United States, doctors and nurses pull up your patient record based on your name and birthdate.  Sometimes it’s not your chart they pull up.  This is not only a healthcare problem; it’s a data science problem.

Feb 13, 2018

This essay explores the changing significance of gender in fiction, asking especially whether its prominence in characterization has varied from the end of the eighteenth century to the beginning of the twenty-first. The authors found that while gender roles were becoming more flexible, the space actually allotted to (real, and fictional) women on the shelves of libraries was contracting sharply.

Diagram of a timeline of events for generating a recommendation for a sample learner
Mar 21, 2017

The path towards a more democratized learner success model for MOOCs has been hampered by a lack of capabilities to provide a personalized experienced to the varied demographics MOOCs aim to serve.  Primary obstacles to this end have been insufficient support of real-time learner data across platforms and a lack of maturity of recommendation models that accommodate the learning context and breadth and complexity of subject matter material in MOOCs. In this paper, we address both shortfalls with a framework for augmenting a MOOC platform with real-time logging and dynamic content presentation capabilities as well as a novel course-general recommendation model geared towards increasing learner navigational efficiency. We piloted this intervention in a portion of a live course as a proof-of-concept of the framework. The necessary augmentation of platform functionality was all made without changes to the open-edX codebase, our target platform, and instead only requires access to modify course content via an instructor role account.

The organization of the paper begins with related work, followed by technical details on augmentation of the platform’s functionality, a description of the recommendation model and its back-tested prediction results, and finally an articulation of the design decisions that went into deploying the recommendation framework in a live course.

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Data Science news

David Bamman

Assistant Professor David Bamman was honored for for his research designing computational methods for natural language processing for fiction.

Aditya Parameswaran

The Sloan Research Fellowship is given to the brightest up-and-coming scientists in the United States.

Ben Arnoldy

MIDS student Ben Arnoldy has been awarded the Fall 2019 Jack Larson Data Science for Good Fellowship for his work involving the use of data science to educate the public on climate change and pollution.

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Assistant Professor Zachary Pardos and his team have developed a machine learning approach that promises to help more community college students position themselves to transfer and succeed at four-year colleges and universities. 

Parameswaran accepts the 2019 VLDB Early Career Research Contributions Award.

Assistant professor at the Berkeley School of Information, Aditya Parameswaran, has been awarded the 2019 Very Large Data Bases Early Career Award.

Santamaria Ots locating cities in need of relief supplies.

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

Professor Marti Hearst

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

Josh Blumenstock at the Artifical Intelligence for Economic Development conference

Prof Blumenstock received the Faculty Award for Research in the Public Interest for his research at the intersection of machine learning and development economics.

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