Data and methods for identifying artificial intelligence-related patents
This paper evaluates existing approaches to identifying artificial intelligence (AI)-related patents and introduces a novel, scalable framework to improve classification performance. Motivated by the growing reliance on patent data in innovation research, we assess widely used methods and document their substantial performance limitations. To address these challenges, we develop a CPC-informed label-refinement framework inspired by positive-unlabeled (PU) learning to construct high-quality training data.
From Hotspots to Help-Spots: An AI-enabled, Community-centered Risk-resource Mapping
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This project will focus on Baltimore City and/or Prince George’s County, with the final study site selected in consultation with researchers and community partners. The project’s central contribution will be the integration of two complementary geospatial frameworks: risk terrain and resource terrain.
Learning from Earnings Calls: Graph-Based Conversational Modeling for Financial Prediction
Earnings conference calls are valuable venues for business communication. Empirical research has shown that the content of earnings calls contains predictive signals about future market risks, which has motivated a line of computational studies that utilize earnings transcripts for financial forecasting tasks. However, earnings call transcripts are typically very long, and the predictive signals within them are often sparsely distributed across different sections of the transcript.
Business Meets Journalism: UMD Experts Tackle Local News Crisis
Smith School and Merrill College faculty joined journalists to explore innovative business models, AI tools and cross-disciplinary solutions to help local news organizations adapt to declining revenue, shrinking readership and the disruptive impact of digital platforms.
20 Faculty Teams Awarded Smith Internal Research Grants
The Smith School has awarded 2025 Smith Internal Research Grants to 20 faculty-led teams to support high-impact research in areas including AI, entrepreneurship, labor markets, corporate communication, and online learning, fostering innovation across disciplines.
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.
All in on AI
The Robert H. Smith School of Business at the University of Maryland is pioneering AI education to meet rising industry demand. The school's new AI center and specialized programs prepare students to address business challenges with AI, ensuring graduates' market relevance.
UMD Smith Launches Large Language Model (LLM) Training Workshop
Government and healthcare professionals can now enroll in a Large Language Model (LLM) training workshop at the University of Maryland's Smith School of Business. Led by expert Kunpeng Zhang, the program focuses on industry-specific accuracy and data security. Secure your spot for August 2-23, 2024.
Smith School Revolutionizes AI Job Tracking with AI-Powered Tool
The University of Maryland’s Robert H. Smith School of Business is leading the charge to understand how artificial intelligence is impacting jobs in the U.S. economy with the world’s first attempt to map the creation of AI-skilled jobs. The Smith School just launched UMD-LinkUp AI Maps, a dynamic tool that uses job posting data to analyze the spread of jobs requiring AI skills across the country – by sector, state and more granular geographic levels.
Smith Secures $150K for AI Initiative for Capital Market Research
GRF CPAs & Advisors has awarded $150,000 to the University of Maryland’s Robert H. Smith School of Business to seed an AI Initiative for Capital Market Research.