Smith School Presents AI Research At Lightning Speed
The Robert H. Smith School of Business hosted AI Lightning Talks on Oct. 23, 2025, featuring 15 faculty and PhD presenters sharing fast-paced insights into recent AI research topics, from healthcare and entrepreneurship to generative AI and visual design.
Smith Research Recognized for ‘Potential to Create Positive Societal Changes’
Doctoral student Eunseong Jang at the Smith School developed new statistical models to expand incomplete datasets, giving law enforcement and policymakers tools to fight drug trafficking. His NSF-funded research, guided by Margret Bjarnadottir and S. Raghu Raghavan, earned a $6,000 award for its societal impact.
Research from Smith Looks at Progress and Prospects with Large Language Models and Synthetic Health Data
In a JAMIA Open study, the Smith School’s Margrét Bjarnadóttir explores how large language models can generate synthetic health data to improve predictive models while addressing algorithmic bias and advancing health equity.
More than 37K Enroll in Smith AI Course for Career Empowerment
More than 37,000 learners worldwide have joined the Smith School’s free AI and Career Empowerment course—designed for transitioning federal workers but open to all—building AI literacy and career skills through expert-led, self-paced modules.
Ending Poverty, Classroom Tools and Job Algorithms
The fourth annual Workshop on AI & Analytics for Social Good gathered nearly 100 experts to explore AI’s societal impact, featuring 17 speakers and topics like wildfire containment, algorithmic fairness, empathy in data labeling, and AI-driven text analysis.
Improved LISA Analysis for Zero-Heavy Crack Cocaine Seizure Data
Local Indicators of Spatial Association (LISA) analysis is a useful tool for analyzing and extracting meaningful insights from geographic data. It provides informative statistical analysis that highlights areas of high and low activity. However, LISA analysis methods may not be appropriate for zero-heavy data, as without the correct mathematical context the meaning of the patterns identified by the analysis may be distorted. We demonstrate these issues through statistical analysis and provide the appropriate context for interpreting LISA results for zero-heavy data.
Large language models and synthetic health data: progress and prospects
There is growing interest in the application of machine learning models and advanced analytics to various healthcare processes and operations, including the generation of new clinical discoveries, development of high-quality predictions, and optimization of administrative processes. Machine learning models for prediction and classification rely on extensive and robust datasets, particularly for deep learning models common in health, creating an urgent need for large health datasets.
Smith School’s Bjarnadóttir Earns Highest Icelandic State Honor for Research Impact
Margrét Bjarnadóttir, associate professor at the Smith School, was awarded the Knight’s Cross of the Icelandic Order of the Falcon for transformative research on pay equity.
Smith School Hosts Workshop on AI and Analytics for Social Good
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.
Mapping the Cocaine Supply Chain
A team of UMD researchers is studying how to map and disrupt the cocaine supply chain. The research is supported by the National Science Foundation (NSF) and is led by the Smith School’s S. Raghu Raghavan.