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.
TechFest 2025: DOIT-hosted Forum Tackles Growing Risks From AI, Data and Cybersecurity
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.
2nd Annual AI Symposium Addresses the Challenges and Benefits of Artificial Intelligence
Experts from business, government, and nonprofits shared insights on AI’s challenges, opportunities, and governance at the Smith School’s 2nd Annual AI Symposium, highlighting innovation in healthcare, finance, manufacturing, and legal compliance.
Smith Research Develops Model to Address Product Life Cycle Forecasting
Smith’s Xiaojia Guo co-authored a study on forecasting demand for new products using Bayesian model averaging. The research offers a robust, flexible model for predicting life cycles of fast-evolving items like fashion, news topics and digital content.
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.
Date
Location
Contact
Online Marketplaces: Beyond the Number of Buyers and Sellers
New online marketplaces often rely on network effects to grow, but inviting prominent sellers can shape their long-term reputation. Research from Wedad Elmaghraby and Ashish Kabra shows that high-quality sellers attract premium buyers, while lower-quality sellers foster price-sensitive markets.
Flipping the Deepfake Narrative
Professors Siva Viswanathan, Balaji Padmanabhan, and PhD student Yizhi Liu at UMD’s Smith School developed a patent-pending deepfake method to detect and mitigate bias in decision-making. Their study shows how AI-generated images can improve fairness in hiring, healthcare, and criminal justice.
Alumna Credits Contacts at Smith, UMD for Help With App Development
Smith School alumna Lauren Der ’19 began her career in federal consulting before transitioning to the public sector as a Change Management Specialist for Montgomery County Government. She also developed Opsy, a health-tracking app inspired by her personal health journey.
Diffusion of AI Jobs Across Sectors
AI job postings in the U.S. surged 68% since ChatGPT’s launch, despite a 17% decline in overall job postings. UMD-LinkUp AI Maps, led by Smith’s Anil K. Gupta, reveals AI’s rise as firms prioritize AI roles over traditional IT jobs.
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.