University of Maryland’s Smith School hosted its annual AI and Analytics for Social Good Workshop, convening experts to showcase data-driven research addressing societal challenges, advancing responsible AI, informing policy and preparing students for an evolving, technology-driven workforce.

At the Smith School's TechFest 2025, experts in AI, data, and cybersecurity explored deepfakes, ethical risks, mental health, and human-centered leadership, urging students to balance innovation with responsibility in tackling rising digital threats across business and society.

On May 10, 2024, the University of Maryland’s Smith School hosted the AI and Analytics for Social Good Workshop, featuring experts from leading institutions discussing the use of analytics for social impact, including misinformation, platform regulation, and educational analytics.

In a world of uncertainty, logistics firms must predict how many resources will be needed to achieve service objectives. According to the recent development of a statistical uncertainty model by Maryland Smith’s Ilya Ryzhov, predicting such costs is within reach.

Maryland Smith’s Ilya Ryzhov is leveraging a three-year grant awarded by the National Science Foundation to continue research on predictive and prescriptive methods for humanitarian logistics and disaster mitigation.

The Smith School is happy to welcome the following new professors to the school: Accounting & Information Assurance Hanna Lee Derek Johnson Decisions, Operations & Information Technologies Tunay Tunca Inbal Yahav Ilya O. Ryzhov Pamela Armstrong Ilchul Yoon Rui Zhao

For B2B sellers, knowing how much the buyer is willing to pay is difficult. New research from Maryland Smith is helping to figure it out.

How do you make good decisions when you don’t have all the information in front of you? New research lays out a framework to learn from indirect or incomplete information to make better decisions.

Managers who rely on computer models to help with decision-making bump into a dilemma when it comes to allocation of scarce resources in complex environments with many moving parts.