Smith Researcher Shows Celebrity Messages Reduce Online Hate
Research coauthored by Smith School assistant professor Eaman Jahani found that targeted prosocial messages on X reduced hate speech and its spread in Nigeria. The large-scale experiment suggests preventive, non-censoring interventions can complement content moderation and influence behavior through social networks.
Smith Students Unleash Custom AI and Web Tools for Local Pet Care Business
Smith School information systems students developed AI-powered tools for local businesses and nonprofits as part of a senior capstone course. The winning team created a customer booking platform, chatbot and business management dashboards for Wild at Heart Pet Care.
TechFest 2026 Spotlights Alumni Expertise, Student Innovation and Outstanding Seniors at Smith
The University of Maryland’s Robert H. Smith School of Business hosted TechFest 2026 on April 22 in Van Munching Hall, bringing together alumni, faculty, and students for an evening of discussion, project showcases and student recognition. Presented by the Department of Decision, Operations & Information Technologies (DO&IT) and the Business & Information Technology Society (BITS), this year’s theme—“Why Choose Information Systems at UMD Smith? The Bridge Between Technology and its Business and Social Contexts”—highlighted the program’s focus on preparing students to lead at the intersection of technology and real-world impact.
Prompt Adaptation as a Dynamic Complement in Generative AI Systems
As generative-AI models become more powerful, organizations will only realize a portion of that improvement unless users learn to adjust how they interact with the models — prompt-adaptation becomes a critical skill for unlocking full value.
To capture the full benefits of new AI technologies, companies should invest not only in the latest models and infrastructure, but also in user training and workflow design — enabling teams to use the new technologies effectively.
Smith School Hosts AI and Analytics for Social Good Workshop, Highlighting Research, Policy and Workforce Implications
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.
Third Annual Symposium Examines AI’s Opportunities and Challenges
At the 2026 AI Symposium on Design and Governance, speakers examined how artificial intelligence is shaping work, healthcare and software, emphasizing human-centered design, emerging skills, ethical oversight and the responsible integration of AI to augment, not replace, human capabilities.
This workshop brings together academics, thought leaders and stakeholders to discuss how analytics can support nonprofit organizations, government entities and social impact organizations in improving their reach and impact through innovative use of data and models. The overarching theme is analytics for doing good.
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Evolution of Ride Services: From Ride- Hailing to Autonomous Vehicles
In recent years the ride service industry has been evolving rapidly, driven by disruptive technologies such as mobile apps, AI, and autonomous vehicles (AVs). While platform-based decentralized ride hailing companies have gained significant market share, vertically-integrated robotaxi services using emerging AVs are starting to enter the market. In this paper, we aim to provide insights about the evolution and the future of ride services studying these two competing business approaches.
Stochastic Gradients: Optimization, Simulation, Randomization, and Sensitivity Analysis
Big data and high-dimensional optimization problems in operations research (OR) and artificial intelligence (AI) have brought stochastic gradients to the forefront. This article provides a view of research and applications in stochastic gradient estimation from multiple perspectives, as seminal advances have come from diverse and disparate research fields, including operations research/management science (OR/MS), industrial/systems engineering (ISE), optimal/stochastic control, statistics, and more recently from the computer science (CS) AI machine learning (ML) community.
Simulation Optimization and Artificial Intelligence
With the relentless increase in computing power and the ubiquitous availability of data in many industries, the fields of simulation optimization and artificial intelligence have emerged at the scientific and engineering forefront in their societal impact, manifested in the pervasiveness of technologies such as large language models, chatbots, digital twins, and agent-based systems. We examine cross-fertilization between simulation optimization and artificial intelligence, with a particular focus on reinforcement learning, highlighting research that has been mutually beneficial.