Biography

Yueyang Zhong is an Assistant Professor of Management Science and Operations at London Business School. Before joining LBS, she worked as a postdoctoral researcher under the guidance of Prof. Vahideh Manshadi and Prof. Rad Niazadeh in 2024. Yueyang completed her PhD in Management Science and Operations Management at the University of Chicago Booth School of Business in 2023, where she was advised by Prof. Amy Ward and Prof. John Birge. Her PhD dissertation is titled "Many-Server Queueing Models With Applications to Modern Service Operations Management". Prior to her PhD, Yueyang earned her Bachelor’s degree in Industrial Engineering and Economics from Tsinghua University in 2018.

Yueyang's research focuses on advancing modern service systems. She is deeply passionate about the following three foundational research areas:

Strategic Queueing: She investigates human strategic behaviors in queueing systems, examining how system environments influence customer and server behaviors, as well as their interactions, and their implications for system design. Her approach combines stochastic modeling with empirical and experimental studies.
Online Learning in Queueing: She leverages structural results from queueing theory to develop online learning algorithms to optimize decision-making in queueing systems.
Applied Probability and Stochastic Processes: She employs fluid approximation (first-order deterministic approximation) and diffusion approximation (second-order stochastic approximation) to study queueing systems that lack the tractability of exact analysis.

In addition to these foundational research themes, her recent active working areas include:

Information Design in High-Stakes Industries: She explores how information can be strategically designed to enhance outcomes in critical sectors such as mental health and education. Her work integrates theoretical modeling with empirical analysis and field experiments to uncover actionable solutions that improve performance.
Healthcare Operations: She studies the interplay of efficiency, quality, and equity in healthcare delivery systems in the UK. By combining stochastic modeling, machine learning, and empirical methods, she aims to develop operational solutions that optimize resource allocation and improve patient outcomes in an equitable manner.
Robust Mechanism Design in the Digital Economy: She explores robust regulatory approaches for emerging technologies that are not well understood by humans. Her work aims to effectively manage the risks and rewards associated with these technologies, ensuring that their societal impact is both positive and sustainable.

Her research utilizes tools from applied probability, queueing theory, game theory, information theory, optimization, statistics, online learning, and reinforcement learning.

Prospective Students: Yueyang is actively seeking Ph.D. students and short-term research interns passionate about these areas. Candidates with strong backgrounds in mathematics, economics, or statistics are encouraged to contact her via email to explore opportunities.

RESEARCH

PUBLICATIONS

"Learning to Schedule in Multiclass Many-Server Queues with Abandonment," (2024), with John R. Birge and Amy R. Ward. Operations Research.
We introduce a Learn-then-Schedule algorithm for a multiclass many-server queue with abandonment that achieves optimal regret rate by exploiting the asymptotic optimality of a simple, easy-to-implement index policy.

Media Mention: Chicago Booth Review

"Behavior-Aware Queueing: The Finite-Buffer Setting with Strategic Servers," (2023), with Ragavendran Gopalakrishnan and Amy R. Ward. Operations Research.
We develop a queueing game model to examine how strategic servers' work speeds in many-server queueing systems are influenced by managerial decisions concerning staffing, compensation, and customer admission.

Technical Supplement: Some Properties of the Erlang-B and Erlang-C Formulae
Media Mention: Chicago Booth Review, Smith Business Insight
Finalist, 2022 INFORMS IBM Best Student Paper Award

"Some Asymptotic Properties of the Erlang-C Formula in Many-Server Limiting Regimes," (2024), with Ragavendran Gopalakrishnan. Operations Research Letters 54: pp. 107-116.
We provide a complete characterization of the asymptotic behavior of the Erlang-C formula, a cornerstone of a wide range of optimization problems in many-server queueing systems.
"Asymptotically Optimal Idling in the GI/GI/N+GI Queue," (2022), with Amy R. Ward and Amber L. Puha. Operations Research Letters 50(3): pp. 362-369.
We show that an idling policy is asymptotically optimal in a GI/GI/N+GI queue, balancing operational costs and server utilization costs.
"Data-Driven Market-Making via Model-Free Learning," (2021), with YeeMan Bergstrom and Amy R. Ward. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI).
We propose a Q-learning trading strategy for market-making on limit order books, which outperforms benchmarks in backtesting with a collaborating firm.

WORKING PAPERS

"Queueing Versus Surge Pricing Mechanism: Efficiency, Equity, and Consumer Welfare," (2020), with Zhixi Wan and Zuo-Jun Max Shen.
We build a queueing network model to compare surge pricing and virtual queueing mechanisms for managing peak-hour demand on ridesharing platforms, focusing on efficiency and equity metrics.

Finalist, 2021 INFORMS Conference on Service Science Best Student Paper Award

WORKS IN PROGRESS

Naor’s Inequality Reversal When Strategic Customers Meet Strategic Servers, with Ragavendran Gopalakrishnan and Amy R. Ward.
Motivating Long-Term Effort by Information, with Vahideh Manshadi and Rad Niazadeh.
Is Continuity All We Need? A Modeling Approach to Evaluating Relational Continuity in Primary Care, with Naireet Ghosh and Nicos Savva.
Optimal Dispatching for Efficiency and Fairness in Emergency Medical Service Systems, with Jinghai He, Cheng Hua, and Tauhid Zaman.

TEACHING

London Business School

Data Analytics for Management (MiM core): Fall 2024

The University of Chicago Booth School of Business

Applied Regression Analysis (MBA core): Fall 2020, Fall 2021
Operations Management: Business Process Fundamentals (MBA core): Winter 2020
Managing Service Operations (MBA selective): Winter 2022

SERVICES

Academic Services

Journal Ad-hoc Referee: Operations Research, Manufacturing & Service Operations Management, Mathematics of Operations Research, Production and Operations Management, Queueing Systems, Journal of Applied Probability, INFORMS Journal on Computing, European Journal of Operational Research, Operations Research Letters, Service Science, IEEE Transactions.
Conference Referee: INFORMS Conference on Service Science (2023), MSOM Service Management SIG (2024), ACM Conference on Economics and Computation program committee (2025)
Paper Competition Committee: INFORMS Nicholson prize committee (2024), POMS College of Supply Chain Management Best Student Paper Competition (2025)

Industry Experiences

Part-time Consulting, CLEARCOGS (Remote, Spring 2023)
Research Intern, Ads Marketplace team, Pinterest Labs (Remote, Summer 2021)
Research Intern, Data Science Group, Blue Fire Capital, LLC (Chicago, Summer 2019)
Research Intern, Research Center of Innovation and Operations, DiDi (Beijing, Summer 2018)