Key Areas of Research
Data and methods for identifying artificial intelligence-related patents
Research Policy
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. Using this refined dataset, we train a range of machine learning, deep learning, and transformer-based models. Our models substantially outperform existing approaches, including those employed by the USPTO. We further demonstrate the empirical value of more accurate AI patent identification through two applications. First, we find that the release of ChatGPT increased the market valuation of AI patents. Second, we show that firms increased the allocation of innovative effort toward AI technologies following its release. To facilitate future research, we release our training data, source code, and a novel dataset of patent-level predictions (AIPat), which will be continuously updated to reflect the evolving nature of AI innovation.
Tianjun Wu (Nanjing University),
Chao Min (Nanjing University),
Waverly W. Ding (University of Maryland),
Guolong Wang (University of International Business and Economics),
Kunpeng Zhang (University of Maryland)
From Hotspots to Help-Spots: An AI-enabled, Community-centered Risk-resource Mapping
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. The risk terrain layer will identify places where firearm violence risk may be elevated based on spatial, environmental, social, and temporal indicators, such as prior shootings, vacant properties, limited lighting, transportation corridors, housing instability, alcohol outlets, commercial activity nodes, and other built-environment or neighborhood conditions (Brantingham & Brantingham, 1995; Caplan & Kennedy, 2016; Kennedy et al., 2011). The resource terrain layer will identify places where prevention capacity already exists or could be strengthened, including community violence intervention programs, schools, libraries, recreation centers, faith institutions, small businesses, health providers, youth-serving programs, neighborhood associations, and trusted informal leaders.
Principal Investigator: Kunpeng Zhang
Prompt Adaptation as a Dynamic Complement in Generative AI Systems
Information Systems Research, April 2026
As generative-AI models become more powerful, organizations will only realize a portion of that improvement unless users learn to adjust how they interact with the models — prompt-adaptation becomes a critical skill for unlocking full value.
To capture the full benefits of new AI technologies, companies should invest not only in the latest models and infrastructure, but also in user training and workflow design — enabling teams to use the new technologies effectively.
Eaman Jahani (UMD), Benjamin S. Manning (MIT), Joe Zhang (Stanford), Hong-Yi TuYe (MIT), Mohammed Alsobay (Microsoft), Christos Nicolaides (University of Cyprus), Siddharth Suri (Microsoft), David Holtz (Columbia)
Evolution of Ride Services: From Ride- Hailing to Autonomous Vehicles
Management Science
In recent years the ride service industry has been evolving rapidly, driven by disruptive technologies such as mobile apps, AI, and autonomous vehicles (AVs). While platform-based decentralized ride hailing companies have gained significant market share, vertically-integrated robotaxi services using emerging AVs are starting to enter the market. In this paper, we aim to provide insights about the evolution and the future of ride services studying these two competing business approaches. We find that in many cases in larger markets the ride-hailing firm surprisingly gains the upper hand in competition, having higher market share and profits as well as lower service delays and higher prices, even if it has a cost disadvantage. Further, entry of the AV firm into a market with a dominant ride-hailing firm may reduce total vehicle supply and increase customer wait costs. We also find that when customers are impatient, the entry of a high cost AV firm may lead to a decrease in social welfare despite introducing competition, suggesting that regulators should be careful about introduction of robotaxi services in a market if they are not sufficiently cost efficient. From a broader perspective, our results demonstrate that platform business models in general may have significant strategic advantages over firms with traditional vertically-integrated models under competition, and platforms' dominance in a market may even result in welfare gains.
Daehoon Noh (Korea University), Tunay I. Tunca (UMD, Smith), Yi Xu (UMD, Smith)
Simulation Optimization and Artificial Intelligence
Foundations and Trends in Optimization, December 2025
With the relentless increase in computing power and the ubiquitous availability of data in many industries, the fields of simulation optimization and artificial intelligence have emerged at the scientific and engineering forefront in their societal impact, manifested in the pervasiveness of technologies such as large language models, chatbots, digital twins, and agent-based systems. We examine cross-fertilization between simulation optimization and artificial intelligence, with a particular focus on reinforcement learning, highlighting research that has been mutually beneficial. This work presents examples of the synergies between the fields, followed by real-world applications and case studies, including some futurist and forward-looking concepts.
Yijie Peng (Peking University), Chun-Hung Chen (George Mason University, and Michael C. Fu (University of Maryland)
Stochastic Gradients: Optimization, Simulation, Randomization, and Sensitivity Analysis
IISE Transactions, February 2026
Big data and high-dimensional optimization problems in operations research (OR) and artificial intelligence (AI) have brought stochastic gradients to the forefront. This article provides a view of research and applications in stochastic gradient estimation from multiple perspectives, as seminal advances have come from diverse and disparate research fields, including operations research/management science (OR/MS), industrial/systems engineering (ISE), optimal/stochastic control, statistics, and more recently from the computer science (CS) AI machine learning (ML) community.
Michael C. Fu (University of Maryland), Jiaqiao Hu (Stony Brook University, and Katya Scheinberg (Georgia Institute of Technology)
Online Learning with Survival Data
February 2026
Decision-makers frequently use adaptive experiments to optimize time-to-event outcomes, such as accelerating healthcare screenings among patients who are not up to date or delaying customer churn. A common choice to run these adaptive experiments is a multi-armed bandit with a dichotomized outcome -- an experimenter sets some threshold (e.g. 1 month) and then uses the algorithm to identify the intervention with better performance on the dichotomized outcome (e.g. which algorithm maximizes the proportion of participants who get up to date on screening within a month of outreach). We introduce "survival bandits," a principled class of algorithms that integrate the Cox proportional hazards model to better learn from time-to-event outcomes. Both theoretically and numerically (with a case study on cervical cancer screening), we show that these new algorithms have the potential to greatly improve adaptive experimentation for decision makers across industries who seek to speed or slow an event of interest.
Arielle Anderer (Assistant Prof, Cornell), Hamsa Bastani (Associate Prof, UPenn Wharton), John Silberholz (Assistant Prof, UMD Smith)
Backfiring AI? AI Deployment in Workplace
Management Science
AI in the workplace has the potential to change the competitive dynamics among employees. The AI system can learn from high-performing employees and make that knowledge available to others. In a competitive environment, this can disincentivize high-performing employees and ultimately backfire, leading to a decline in overall firm productivity. Our results suggest that some ostensibly simple solutions, such as guaranteeing or increasing wages for adversely affected employees, may not effectively solve the problem, and firms would have to judiciously choose optimal AI efficacy levels to achieve better outcomes.
Di Yuan (Assistant Professor, Auburn University), Manmohan Aseri (Assistant professor, University of Maryland), Narayan Ramasubbu (Professor, University of Pittsburgh)
Market Formation, Pricing, and Revenue Sharing in Ride Hailing Services
Manufacturing & Service Operations Management, September 2025
Problem definition: We empirically study the market for ride-hailing services. In particular, we explore the following questions: (i) How do the two-sided market and prices jointly form in ride-hailing marketplaces? (ii) Does surge pricing create value and for whom? How can its efficiency be improved? (iii) Can platforms' strategy on revenue sharing with drivers be improved? (iv) What is the value generated by ride-hailing services, including hosting rival taxi services on ride-hailing apps? Methodology/Results: We develop a discrete choice model for the formation of mutually dependent demand (customer side) and supply (driver side) that jointly determine pricing. Using this model and a comprehensive data set obtained from the largest mobile ride platform in China, we estimate customer and driver price elasticities and other factors that affect market participation for the company's two main markets, namely basic ride-hailing and Taxi services. Based on these estimation results and counterfactual analysis, we demonstrate that surge pricing improves customer and driver welfare as well as platform revenues, while counterintuitively reducing Taxi revenues on the platform. However, surge pricing should be avoided during non-peak hours as it can hurt both customer and platform surplus. We show that platform revenues can be improved by increasing drivers' revenue share from the current levels. Finally, we estimate that the platform's basic ride-hailing services generated customer value equivalent to 13.25 Billion USD in China in 2024, and hosting rival Taxi services on the platform boosted customer surplus by 3.6 Billion USD. Managerial Implications: Our empirical framework provides ride-hailing companies a way to estimate demand and supply functions, which can help with optimization of multiple aspects of their operations. Our findings suggest that ride-hailing platforms can improve profits by containing surge-pricing to peak hours only and boosting supply by increasing driver compensation. Finally, our results demonstrate that restricting ride-hailing services create significant welfare losses while including taxi services on ride-hail platforms generate substantial economic value
Liu Ming, Tunay I. Tunca, Yi Xu, and Weiming Zhu
Unintended Consequences of Closing Pay Gaps Across Multiple Groups: A Formal Modeling and Simulation Analysis of Allocation Methods
Organization Science, October 2025
In recent years, many firms have prioritized both pay equity (i.e., closing pay gaps associated with target groups such as women and racial minorities) and equitable representation (i.e., ensuring these target groups are fairly represented across a firm’s hierarchy). We use formal modeling and simulations to show how efforts to close pay gaps across multiple groups can undermine equitable representation. Specifically, our analysis suggests that pressure for pay equity creates a cost-based financial incentive to enact a subtle form of tokenism: A firm may minimize the cost of closing pay gaps if it maintains a workforce with a small number of minority women whom it pays well in order to compensate for underpaying larger numbers of majority women and minority men who resemble each other in terms of job attributes and personal qualifications. A firm can avoid these outcomes if it focuses on ensuring that employees from target groups are equitably rewarded for job attributes and personal qualifications rather than minimizing cost. But an equitable-rewards approach can be substantially more expensive than a cost-minimization approach, especially if pay gaps are larger in high-wage jobs or if there are many target groups. We conclude by offering testable empirical predictions and recommending a practical solution, namely to include terms for intersectional categories (e.g., minority women) in the regressions used to estimate pay gaps.
David Anderson (Villanova University); Margret Bjarnadottir (University of Maryland);
David Ross (University of Florida)