Upload your resume. PhdFit's six research agents compare your background with faculty profiles, recent publications, lab focus, and outreach opportunities, then rank professors with evidence you can review.
Ask how her lab is extending interpretability methods into fairness audits for real-world AI systems.
New York University · Technology, Operations, and Statistics Department
Active 2000–2025
Mor Armony is the Vice Dean of Faculty, Harvey Golub Professor of Business Leadership, and Professor of Technology, Operations & Statistics at New York University Stern School of Business. She teaches courses in operations management and service operations. Her primary research interests include the management of patient flow in healthcare, optimization of customer experience in contact centers, and stochastic modeling of various operations. Her scholarly articles have been published in numerous reputable outlets, including Management Science, Operations Research, and Queueing Systems. Before joining NYU Stern in 1999, Professor Armony served as a consultant for Lucent Technologies and AT&T. She also developed mathematical models for predicting financial indexes at Eventus, Israel. She holds a Bachelor of Science in mathematics and statistics and a Master of Science in statistics from the Hebrew University of Jerusalem. Additionally, she earned a Master of Science and a Ph.D. in engineering-economic systems and operations research from Stanford University.
Telemedicine versus In-Person Outpatient Care: Equilibrium, Capacity, and Quality Differentiation
SSRN Electronic Journal · 2025-01-01 · 1 citations
Hospital vs. Home Care: Trading off Predischarge and Postdischarge Infection and Mortality Risks
Manufacturing & Service Operations Management · 2025-10-14
Hayley B. Gershengorn
University of Miami
Michelle N. Gong
Montefiore Medical Center
Yunchao Xu
New York University
Carri W. Chan
Avishai Mandelbaum
Technion – Israel Institute of Technology
PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.
Problem definition: Determining the optimal length of stay (LOS) and posttreatment location is critical for hematology-oncology (blood cancer) patients, who are highly vulnerable to life-threatening infections. Early discharge to home care reduces infection risk, whereas extended hospital observation minimizes mortality risks if an infection occurs. We address this trade-off by developing LOS optimization models tailored to these patients. Methodology/results: We develop a newsvendor-type model to explore how infection and mortality risks influence optimal LOS of individual patients. We further consider the social optimization problem in which capacity constraints limit the ability of hospitals to keep patients for the entirety of their optimal LOS. We find that, in the optimal solution to the fluid model used to approximate the original stochastic system, each type of patient is discharged at at most two discrete time points, one of which might be equal to zero or to the optimal uncapacitated length of stay. Based on this analysis, we propose an online index-based speedup policy (ISP) to guide patient discharge decisions. Managerial implications: Our model enables physicians to personalize LOS based on patients’ risk profiles and dynamically adapt to hospital capacity constraints. In a case study, we show that around 75% of the patients need some observation, and a speedup-only policy that discharges all patients at the same discrete time point is optimal for 90% of patient types during high demand. Adopting ISP can reduce the patient mortality rate by 27.7% compared with current practice. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0189 .
Manufacturing & Service Operations Management · 2024-08-19 · 10 citations
Problem definition: Contrary to traditional queueing theory, recent field studies in B2C services indicate that pooled queues may be less efficient than dedicated queues. Methodology/results: We use two online experiments in the healthcare delivery context to replicate this finding and assess the interplay of servers’ customer ownership and queue length awareness as potential underlying mechanisms. We operationalize customer ownership as the extent to which servers feel ownership toward their customers and queue length awareness as the extent to which servers are able to accurately quantify their number of customers. We find that, following a change in queue configuration, dedicated queues outperform pooled queues with respect to processing speed without sacrificing quality. The reduction in speed is partially mediated by the servers’ queue length awareness and partially suppressed by their ownership of customers in queue. The former is because servers turn out to be less likely to underestimate their load, which makes them work faster. The latter is because ownership of customers in queue may distract servers from the customer in service. When the queue configuration changes from a dedicated to a pooled one, the shorter processing times and higher levels of queue length awareness persist across the change, unlike the higher ownership of customers in the queue. Managerial implications: In discretionary service settings, switching to a dedicated queue is often beneficial in terms of operational performance, partly because the increased queue length awareness motivates servers to work faster; however, the increased degree of customer ownership of those in queue may distract them and result in a slowdown. Funding: This work was supported by the Wharton Behavioral Lab, the Claude Marion Endowed Faculty Scholar Award, the Wharton-INSEAD Alliance, and the Wharton Dean’s Research Fund. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0202 .
Are Buyers Strategic in Online B2B Reviews?
SSRN Electronic Journal · 2022-01-01
SSRN Electronic Journal · 2021-01-01 · 3 citations
Hospitalization versus Home Care: Balancing Mortality and Infection Risks for Hematology Patients
SSRN Electronic Journal · 2021-01-01 · 3 citations
The Interplay Between Online Reviews and Physician Demand: An Empirical Investigation
Management Science · 2021 · 137 citations
Social media platforms for healthcare services are changing how patients choose physicians. The digitization of healthcare reviews has been providing additional information to patients when choosing their physicians. On the other hand, the growing online information introduces more uncertainty among providers regarding the expected future demand and how different service features can affect patient decisions. In this paper, we derive various service-quality proxies from online reviews and show that leveraging textual information can derive useful operational measures to better understand patient choices. To do so, we study a unique data set from one of the leading appointment-booking websites in the United States. We derive from the text reviews the seven most frequently mentioned topics among patients, namely, bedside manner, diagnosis accuracy, waiting time, service time, insurance process, physician knowledge, and office environment, and then incorporate these service features into a random-coefficient choice model to quantify the economic values of these service-quality proxies. By introducing quality proxies from text reviews, we find the predictive power of patient choice increases significantly, for example, a 6%–12% improvement measured by mean squared error for both in-sample and out-of-sample tests. In addition, our estimation results indicate that contextual description may better characterize users’ perceived quality than numerical ratings on the same service feature. Broadly speaking, this paper shows how to incorporate textual information into an econometric model to understand patient choice in healthcare delivery. Our interdisciplinary approach provides a framework that combines machine learning and structural modeling techniques to advance the literature in empirical operations management, information systems, and marketing. This paper was accepted by David Simchi-Levi, operations management.
Pooling Queues with Strategic Servers: The Effects of Customer Ownership
Operations Research · 2020 · 51 citations
Strategic Behavior in Queues
On Withholding Capacity from Strategic Patients
SSRN Electronic Journal · 2020 · 5 citations
Capacity choice game in a multiserver queue: Existence of a Nash equilibrium
Naval Research Logistics (NRL) · 2019-12-08 · 2 citations
Abstract In many congestion‐prone services, front‐line employees have discretion over the rate at which they serve customers. To evaluate the impact of queue pooling on their decisions, we model the situation as a two‐server, single‐queue symmetric capacity choice game. Gopalakrishnan et al. (2016) characterize the existence of a Nash equilibrium in this game under a requirement on the servers' capacity cost functions, that is, where servers have limited discretion. Without that requirement and when servers are free to choose any service rate, the servers' cost function is ill‐behaved and standard tools for establishing the existence of an equilibrium cannot be applied. We consider a general power capacity cost function with no restriction on the servers' choice of capacity, and rely on a lesser‐known result, namely Tarski's intersection theorem, to establish the existence of a symmetric pure‐strategy Nash equilibrium. Comparing settings where queue stability is enforceable versus not, we show that there always exists a Nash equilibrium in the former case, unlike in the latter, and that some of the capacity choices that are equilibria in the former case are no longer equilibria in the latter. Our analysis highlights the criticality of the enforceability of system stability on equilibrium outcomes.
Galit B. Yom‐Tov