News Room

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

New Research Cracks the Code on Selling Power of TikTok Video Ads
News Release

BALTIMORE, MD, March 6, 2025 – Even though the future of TikTok in the U.S. hangs in the balance, a groundbreaking study in the INFORMS journal Marketing Science introduces a game-changing algorithm that predicts which TikTok ads will drive sales before they even go live.

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The 3 biggest things to know about a potential U.S.-Ukraine minerals deal
Media Coverage

Contentious minerals deal has sparked a war of words between Trump and Zelensky, and could be announced later this week

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How tariffs can hurt American supply chains | GUEST COMMENTARY
Media Coverage

Americans have probably heard the word “tariffs” more in the past month than in the past four years — and for good reason. Tariffs are central to President Donald Trump’s economic playbook, despite opposition from mainstream economists and trade experts

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Resoundingly Human Podcast

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

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INFORMS in the News

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Egon Balas: 1922–2019

Egon Balas: 1922–2019

News Release, March 19, 2019

INFORMS is saddened to learn of the passing of INFORMS Fellow and esteemed member of the Carnegie Mellon University faculty, Egon Balas, who overcame an early life in the shadow of the volatile political dynamic of World War II-era Romania to become a leader in the field of operations research.

NYPD's Patternizr crime analysis tool raises AI bias concerns

NYPD's Patternizr crime analysis tool raises AI bias concerns

Search Business Analytics , March 14, 2019

The New York Police Department has touted the successful use of its homegrown crime analysis tool to identify potential criminals, but while the pattern recognition tool highlights the widespread potential for advanced analytics, it also raises questions around AI bias. Patternizr, a set of machine learning models developed in-house at the NYPD, is the first crime analysis tool of its kind to be used in law enforcement. It searches through hundreds of thousands of crimes across all 77 precincts in the department's database to find patterns.

Bracketology isn't just for March Madness. It can save lives, save money and solve problems

Bracketology isn't just for March Madness. It can save lives, save money and solve problems

Fox News, March 17, 2019

In a new editorial, Sheldon Jacobson, INFORMS member and professor at the University of Illinois at Urbana-Champaign, discusses how the sophisticated mathematical modeling that is used to increase the odds of having a perfect – or even good – basketball bracket is the same technique used to solve the most complex challenges in both industry and government. 

Modern policing: Algorithm helps NYPD spot crime patterns

Modern policing: Algorithm helps NYPD spot crime patterns

AP News, March 10, 2019

When a syringe-wielding drill thief tried sticking up a Home Depot near Yankee Stadium, police figured out quickly that the crime seemed familiar, and matched it to an identical crime that occurred miles away in Manhattan. The match, though, wasn’t made by an officer looking through files. It was done using Patternizr, pattern-recognition computer software developed by the New York Police Department that allows crime analysts stationed in each of the department’s 77 precincts to compare robberies, larcenies and thefts to hundreds of thousands of crimes logged in the NYPD’s database, transforming their hunt for crime patterns with the click of a button.

New study carefully explores the challenges and current concepts in measuring the success of online advertising

New study carefully explores the challenges and current concepts in measuring the success of online advertising

Graphic Arts Magazine, March 7, 2019

A comprehensive new research study from the INFORMS journal Marketing Science identifies challenges for current measurement practices for online advertising, while calling for better ways to address so-called “unobservable” factors. The key takeaway, according to its authors, is that current “observational” approaches to digital advertising measurement may not be as effective as large-scale randomized control trials.

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