Featured Projects

Rapid fabrication technologies, commonly known as “3D Printing,” enable the rapid creation of physical objects based on digital designs. Scanning technologies enable the opposing process, the creation of digital models derived from physical objects. This site provides information resources on 3D printing and scanning, and on library research projects regarding these technologies.

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The United States Census and the Program on Informatics at the Massachusetts Institute of Technology are convening a series of workshops to examine computational, social-scientific, statistical, and informatic challenges to building the next generation of official statistics. These workshops bring together select groups of experts in universities, industry, and the U.S. government to explore diverse approaches to engaging big data to inform a series of selected exemplar use cases faced by statistical agencies.

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Virtual learning environments are an increasingly important component of individualized learning in STEM domains. New technologies (including biometry and neuroimaging) provide new opportunities to unobtrusively measure student engagement and learning at scale. This project utilizes these technologies to provide foundational knowledge of the ways in which measures of implicit learning from lab settings, ubiquitous sensors, and big data might be linked to explicit learning to develop games and online educational systems that are adaptive to diverse learners.

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The drawing of electoral districts is among the least transparent processes in democratic governance. All too often, redistricting authorities maintain their power by obstructing public participation. The resulting districts embody the goals of politicians to the detriment of the representational interests of communities and the public at large. We have developed DistrictBuilder to increase participation and transparency in the electoral process.

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This project will help establish a formal linkage between scholarly publications and the underlying research data by integrating the OJS PKP and Harvard Dataverse Network systems.

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This project defines and measures privacy in mathematical and legal terms, and explores alternate definitions of privacy that may be more general or more practical. The research aims to provide a better understanding of the practical performance and usability of a variety of algorithms for analyzing and sharing privacy-sensitive data. The project will develop secure implementations of these algorithms and legal instruments, which will be made publicly available.

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Mission

The Program on Information Science seeks to solve emerging problems in information management that are essential to support new and innovative services, and to amplify the impact that MIT can have on the development of information science, information policy, and scholarly communication through participation in the development of standards, policy, and methods related to information science and information management.

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Upcoming Events

Latest News

Program Welcomes First Visiting Scholar - Philip Cohen, PhD.

Program Welcomes First Visiting Scholar - Philip Cohen, PhD.

October 3, 2018

The program is happy to welcome Dr. Philip Cohen to MIT for the Fall 2018 semester.  He is the first Visiting Scholar for the program and will be onsite here one week each in September, November, and January during IAP.  In November he will present a 1 hour talk which will be open to the public.  When not on site, Dr. Cohen will be at his home institution, the University of Maryland.  While here this semester his research will be focused on open peer review and the structure of publishing in the social sciences.

 

More about Dr. Cohen:...

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New article. Our framework for algorithmic fairness just published in a special issue on AI ethics

July 13, 2018

ABSTRACT

In this article, we recognize the profound effects that algorithmic decision making can have on people’s lives and propose a harm-reduction framework for algorithmic fairness. We argue that any evaluation of algorithmic fairness must take into account the foreseeable effects that algorithmic design, implementation, and use have on the well-being of individuals. We further demonstrate how counterfactual frameworks for causal inference developed in statistics and computer science can be used as the basis for defining and estimating the foreseeable...

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New Undergraduate Research Intern Joins the Program for Summer 2018

July 12, 2018

The program supports undergraduate and graduate internships, research affiliates, research assistants, and postdoctoral fellows. (For more information about internships see this page).

 

This summer the program is pleased to welcome Dylan Sam as a new Undergraduate Research Intern. 

 

Dylan is a research assistant and rising second year student at Brown University, currently...

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New Graduate Research Intern Joins the Program for Summer 2018

July 12, 2018

The program supports undergraduate and graduate internships, research affiliates, research assistants, and postdoctoral fellows. (For more information about internships see this page).

This summer the program is pleased to welcome Jessica Chapel as a new Graduate Research Intern. 

 

Jessica Chapel is a second-year student in the Simmons SLIS program focusing on digital libraries and archives...

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First UROP Joins Information Science Program for Spring 2018

First UROP Joins Information Science Program for Spring 2018

February 23, 2018

The program supports undergraduate and graduate internships, UROPS, research affiliates, research assistants, and postdoctoral fellows. (For more information about internships see this page).

This semester the program is pleased to welcome Sara Wilson as the first UROP of many to come.  Sara is sponsored through a joint program with the lab of...

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