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.