Faculty Profiles

My research focuses on advancing decision-making through analytics, operations management, and interdisciplinary methods, addressing challenges in healthcare, transportation, clean energy, and sustainability. I integrate mathematical modeling, simulation, queueing networks, machine learning, and data-driven approaches to create practical solutions that benefit policymakers, businesses, and communities. My teaching interests center on analytics, statistics, and technology-driven business education, with an emphasis on student-centered, experiential learning that bridges theory and practice.

I actively contribute to the academic community through journal reviewing, conference leadership, and graduate student supervision. I have organized and chaired sessions at international conferences such as INFORMS and DSI, and I serve as a reviewer for top journals, including Production and Operations Management (POMS),Transportation Research Part E (TRE), and Computers & Industry Engineering. I also collaborate with industry partners on data-driven projects that connect research to practice, while mentoring graduate and undergraduate students in both research and applied analytics initiatives. I earned my Ph.D. in Operations Management from McGill University, an M.Sc. in Mathematical and Computational Finance from the University of Oxford, and a B.Sc. in Computational Mathematics from Nanjing University. I have also passed all three levels of the Chartered Financial Analyst (CFA) program.

My research is centered on advancing decision-making theory through a multidisciplinary, data-driven approach, with an emphasis on creating sustainable social, environmental, and economic value. This is accomplished by integrating expertise in mathematics, analytics, statistics, operations management, and economics.

Thematically,my work advances healthcare operations by optimizing patient flow, designing incentive-based payment models, and leveraging big data for improved outcomes; supports sustainable urban mobility through energy-efficient EV routing, charging infrastructure, and smart traffic systems; contributes to clean energy transitions with prosumer-based models and renewable integration; and tackles water sustainability with forecasting and wastewater reuse strategies. Looking ahead, my future research will deepen the use of machine learning and big data in personalized medicine, expand models for sustainable transportation and net-zero energy systems, and refine operations management approaches to enhance system reliability, efficiency, and resilience across industries, with an emphasis on real-world impact and scalable solutions.

My teaching interests center on analytics, statistics, and technology-driven business education, with a focus on student-centered, experiential learning. I design and teach courses across modalities—online, hybrid, and in-person—that integrate real-world, project-based applications and industry collaborations. By incorporating case-based learning, AI, and emerging data-driven tools, I prepare students to bridge theory and practice while fostering adaptability and lifelong learning. My goal is to equip students with both the technical expertise and critical thinking skills needed to thrive in an evolving workplace.