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
Steven Weber
Professor of the Graduate School
International politics, international business, and the information economy; Cybersecurity; Behavioral economics within Information Systems

Recent Publications

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

Photo via CITRIS and the Banatao Institute

Professors Hany Farid and Joshua Blumenstock have been awarded seed funding for their projects designed to mitigate the COVID-19 crisis by CITRIS and the Banatao Institute.

Josh Blumenstock

Professor Josh Blumenstock is leading a team that has received a grant to investigate and address eviction spikes and displacement risks related to COVID-19.

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Several young faculty have receive presitigous awards and fellowships in 2020: Aditya Parameswaran, Josh Blumenstock and David Bamman.

Josh Blumenstock

The award will support Blumenstock’s work on a new paradigm of algorithmic decision-making  that prioritizes social impact.

David Bamman

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

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