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.
Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts
We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and post-launch distributional forecasts. Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product.
Marketplace Expansion Through Marquee Seller Adoption: Externalities and Reputation Implications
In the race to establish themselves, many early-stage online marketplaces choose to accelerate their growth by adding marquee (established brand name) sellers. We study the implications of marquee seller entry on smaller, unbranded sellers in a marketplace when both unbranded sellers and marquee sellers can vary vertically across reputation (referred to as sellers’ quality). While recent literature has shown that higher-quality unbranded sellers fare better than their lower-quality peers, we posit that this may not hold for entrants of any quality.