News Room

A collection of press releases, audio content and media clips featuring INFORMS members and their research.

A closeup shot of the Capitol dome with a flag flying in the center
Media Coverage

In the run-up to the 2026 midterm elections, the political parties in many states are working to redefine their congressional district maps to gain every possible edge. From California and Texas to Tennessee and Virginia, redistricting efforts have taken center stage. The Supreme Court has sanctioned partisan gerrymandering, and the system has evolved to one in which state legislature majorities get to determine who is most likely to fill those seats in Congress.

In short, gerrymandering has become a central feature of the system, not a bug. But what if we rethink the structure entirely?

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A woman stands in front of a standing desk, her back to the camera. She is dressed casually. She is walking on an under the desk treadmill and looks at a video conferencing call on one of her monitors. She has very long dark hair in a ponytail down her back. A tv sits in the lefthand side of the composition and there is a window in the back behind the desk.
News Release

For years, remote work has been viewed as one of the most important drivers of employee satisfaction. New research suggests that assumption may be giving remote work too much credit.

A study published in the INFORMS journal Management Science found that while remote employees often report higher job satisfaction, much of that advantage disappears when researchers account for factors such as workplace culture, trust in management, communication and opportunities for professional development.

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Media Coverage

A new study from INFORMS suggests warehouse robots perform better when they work together instead of operating independently.

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Resoundingly Human Podcast

An audio journey of how data and analytics save lives, save money and solve problems.

Media Contact

Jeff Cohen
Chief Strategy Officer
INFORMS
Catonsville, MD
[email protected]
443-757-3565

INFORMS in the News

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Using Twitter to predict TV program ratings

Science Newsline, August 2, 2016

INFORMS members Professor Xiao Liu of New York University and Professors Param Vir Singh and Kannan Srinivasan of Carnegie Mellon University conducted a study on which digital platforms are the most effective at gauging the success of a TV program. The study, which will be published in the INFORMS journal Marketing Science, found that Twitter is significantly more effective than other platforms, including Google Trends, Wikipedia, IMDB, and the Huffington Post, at predicting TV ratings.

Clarkson University recognized by INFORMS Rothkopf Rankings

North Country Now, August 6, 2016

The “Rothkopf Rankings,” published in May 2016 in the INFORMS journal Interfaces, recognized Clarkson University as a top U.S. school for contributions to operations research practice literature. 

Maximizing 2016 Olympics’ coverage

The New York Times, August 18, 2016

Press covering the Olympic Games in Rio this summer faced multiple challenges in their efforts to attend as many events as possible, including event timing and location, weather, and travel logistics. Carnegie Mellon professor and INFORMS member Michael Trick helps a New York Times reporter create a plan to approach his coverage of the 2016 games.

Why we ignore security alerts

Naked Security, August 19, 2016

Naked Security, the online news room for data security company Sophos, highlighted an article in the INFORMS journal, Information Systems Research, addressing the effectiveness of system-generated computer security warnings. As much as 87% of these warnings are simply ignored, due to a combination of badly timed interruptions and our inability to multitask.  

Using analytics to predict the 2016 elections

Fox Illinois, August 19, 2016

Starting with the 2000 presidential elections, INFORMS member Sheldon Jacobson, professor with the University of Illinois, and a team of fellow researchers and students, have used a combination of poll numbers, algorithms, and analysis to make a state-by-state assessment of who is most likely to be elected in the upcoming presidential and senate elections.

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