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.
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.